{"as_of":"2026-08-14T06:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44117a3ef3c9f4e736de48e98ed50a34454de6b6b92f2b1f8d09a1cc8cac75c7","coverage":[{"denominator":168,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:14:25.101829Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T17:56:09.884837Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T10:46:31.754330Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"cited_work":{"arxiv_id":"2411.09945","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.09945","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TEESlice: Protecting sensitive neural network models in trusted execution environments when attackers have pre-trained models","venue":null,"work_id":"502d36db-8136-4fec-a18c-dfa5d3a25f15","year":2024},"citing_paper":{"arxiv_id":"2605.03213","last_updated":"2026-05-07T16:46:43Z","snapshot_observed_at":"2026-08-12T21:30:34.125985Z","submitted_at":"2026-05-04T23:09:16Z","title":"When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-07T02:12:30.086152Z"},"links":{"cited_paper":"/paper/2411.09945","citing_paper":"/paper/2605.03213"},"observation_digest":"sha256:05651b811a600cd25add560bbcc47b41ba2e0ac171f20923d51afcfe19843347","observation_id":"023da1d1-28ed-4a64-b2e3-b7d57a9b0d4c","resolution":{"observed_at":"2026-05-12T10:46:31.757375Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"cited_work":{"arxiv_id":"2411.09945","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.09945","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TEESlice: Protecting sensitive neural network models in trusted execution environments when attackers have pre-trained models","venue":null,"work_id":"502d36db-8136-4fec-a18c-dfa5d3a25f15","year":2024},"citing_paper":{"arxiv_id":"2605.03213","last_updated":"2026-05-07T16:46:43Z","snapshot_observed_at":"2026-08-12T21:30:34.125985Z","submitted_at":"2026-05-04T23:09:16Z","title":"When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T17:56:09.884837Z"},"links":{"cited_paper":"/paper/2411.09945","citing_paper":"/paper/2605.03213"},"observation_digest":"sha256:f546779b1ee44c22b18c148378179e3f3811fad0416f1e80bc464238be5d2c2f","observation_id":"87fee389-56d2-44f0-95d9-27fc7bd8b083","resolution":{"observed_at":"2026-05-09T06:55:45.470510Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.09945/citation-record","integrity":"/paper/2411.09945/integrity","json":"/paper/2411.09945/citation-record.json","paper":"/paper/2411.09945"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.668940Z","title":"Knockoff Nets Demo Code","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.668940Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:8f2da0afe87551a664836f3217512e555cc3ed5b0f8d4c0b8b1c61af55e0e2d5","observation_id":"643f8c6e-fd56-42fc-9b24-07c09f37e176","resolution":{"observed_at":"2026-08-12T20:14:24.668940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.673346Z","title":"ML-Doctor Demo Code","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.673346Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:b06302c0a6227672fef86dc949285cc99d62b0db5c711c84e034273b48c2ddd5","observation_id":"3f7f9ba0-eed8-41f8-8422-6b3642ffb75c","resolution":{"observed_at":"2026-08-12T20:14:24.673346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.677750Z","title":"One-time pad","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.677750Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:5e6226dbffec8f7f9e95c00849f31592182506c4efe686f8b7f4dd454cf3e163","observation_id":"dd16bda5-cc4e-4b38-8277-b42355194de2","resolution":{"observed_at":"2026-08-12T20:14:24.677750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.682073Z","title":"Android 7.0 Compatibility Definition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.682073Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:3d89485b6970d5dd817fb52065c7bc695a385fab9cd01f8076e2761e6364e2f9","observation_id":"7d535393-30eb-42e7-8b5a-91c9984d64f5","resolution":{"observed_at":"2026-08-12T20:14:24.682073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.686111Z","title":"Artifact","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.686111Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:0c1e41c68542de764fbb620b861a52e75cb56bf4f90e2b601f886f5ab99ced19","observation_id":"f89c8110-8574-4014-9197-634e2106465d","resolution":{"observed_at":"2026-08-12T20:14:24.686111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.690071Z","title":"Full Supplementary","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.690071Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:2649920bcc862c69c1c5a10738e8316a98bb518b6e2258cc4ea47377f2cb0d6d","observation_id":"86f76f0f-d50b-43df-8827-a7bb9fd450c0","resolution":{"observed_at":"2026-08-12T20:14:24.690071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.694133Z","title":"OP-TEE documentation Raspberry Pi 3","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.694133Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:e4d636f35516fd2fcb09dd8c3724bf3af5a0b18141080a250841305d8275559c","observation_id":"f0af1495-0601-40ee-9b1b-90cda497101f","resolution":{"observed_at":"2026-08-12T20:14:24.694133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.697956Z","title":"Artifact for LLM","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.697956Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:0ab80b9ec4da4f1680e1df06c29776b3cc455b34d9587dd20ef2620991fb118f","observation_id":"2f0d6b9a-d261-48f2-8e86-946fbfa5aebf","resolution":{"observed_at":"2026-08-12T20:14:24.697956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.702189Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.702189Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7c16c3fe220c00465ad2b58bcdb7073fef0c1aa1b000ce782455a268aeb828ba","observation_id":"c44992c3-45b5-4c26-8efa-10053b8d8624","resolution":{"observed_at":"2026-08-12T20:14:24.702189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.705810Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.705810Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:23001b8be736ccf4ca9ffa43301d206b61e41706a5dc44f0fcdab91f0b82b15c","observation_id":"b12ed0ed-43fd-4acc-a402-0279edb7d7ff","resolution":{"observed_at":"2026-08-12T20:14:24.705810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.709574Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.709574Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:9c855914c0bd1c1a9c3c49be5ff10d0f143d9d8377c166aa78c900d98b442b91","observation_id":"4fae174b-e775-495b-b453-237b3e0c304f","resolution":{"observed_at":"2026-08-12T20:14:24.709574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.714166Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.714166Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:190242f764ba4a9197f13c7634427482b5bd1acd04b380583e970b6d4de5beec","observation_id":"cc488c5f-1345-40cb-afbb-917bd4e9d8b8","resolution":{"observed_at":"2026-08-12T20:14:24.714166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.718491Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.718491Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:c2634b96cfa4a79618f6971363d3ad3a338ee91c4098de5b05275f6c92c9e84c","observation_id":"1f8bcc7a-a47f-457b-a245-f806802dece7","resolution":{"observed_at":"2026-08-12T20:14:24.718491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.722325Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.722325Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d4e0601eebee59c5a8f00f5152b761fca3f771eccb29b84c93f5a787423897fa","observation_id":"a2161b3b-23aa-4ea2-9be8-9f18c87072e9","resolution":{"observed_at":"2026-08-12T20:14:24.722325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.725792Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.725792Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:18cc186e86766600f43ac83fadbd0a6f5ee8f9735e5695f6492313546b91fc8b","observation_id":"856f8e72-e3bf-47e7-ae7b-d9dd39b8ffac","resolution":{"observed_at":"2026-08-12T20:14:24.725792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.729702Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.729702Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:77e5e4b2afac1ffb7ef0baf76b2eed34bb97498a77074e3a380585c0d624b6db","observation_id":"96545283-6be7-439e-95e2-069aba818146","resolution":{"observed_at":"2026-08-12T20:14:24.729702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.742597Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.742597Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:147dabfe8d24d0aceb92a30e1ed0e0dfcbcf6c6b2f23eb29f06550a2ff1f2721","observation_id":"dcc6e380-e367-4988-9305-b3d60e4413f8","resolution":{"observed_at":"2026-08-12T20:14:24.742597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.746419Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.746419Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:5636c32f7f679cd8e4075586d7fcb07aaa64ca9eeffd89779fba483f41984f8b","observation_id":"481bb3fd-6efc-48ba-8f59-aedb5849ff10","resolution":{"observed_at":"2026-08-12T20:14:24.746419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29630","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:28.398745Z","title":null,"venue":null,"work_id":"c18dbad9-4a75-4a83-942e-54e5c049d80f","year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.750285Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d4154fc231196ca8a411c026f507daca06d77b4ef3e4a22ad9c679548d724b55","observation_id":"3edb8cec-e2a6-48d0-bb15-1f186075a2aa","resolution":{"observed_at":"2026-08-12T20:14:28.405325Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.754094Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.754094Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:357eb4b15b88d72ebfad8e2d673dfdd538ce3834cbbc8fe71ff00cb012b47280","observation_id":"f27ad94f-619c-406d-9f48-19f9500cdde7","resolution":{"observed_at":"2026-08-12T20:14:24.754094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.757979Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.757979Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:a98397e0fba98b600ed4356625b6151b96c6f46e565295d54806aad7d2cf55c0","observation_id":"5de53a38-292c-4f11-8bc3-26a1878d7d68","resolution":{"observed_at":"2026-08-12T20:14:24.757979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.766081Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.766081Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d63d7b4b691e69aa145907ecc0a8a03d2b38d2b7653f5587deb1f7b11d9db89e","observation_id":"4df8a29b-5e32-42cf-b368-bb201f219607","resolution":{"observed_at":"2026-08-12T20:14:24.766081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.770410Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.770410Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:ad6409566e7fa2d5d48fc365d26d7796addaab550f0138a78b8254aa969e47df","observation_id":"f59f7827-3907-4fad-bc57-4a23c7b61343","resolution":{"observed_at":"2026-08-12T20:14:24.770410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.774768Z","title":"Ng, and Honglak Lee","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.774768Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:3178060bcd0cee2deeaa674e77600c218259aabe57bc5feb652b605167d0d3de","observation_id":"c17396c0-e19f-4383-a35e-123b7649bbcb","resolution":{"observed_at":"2026-08-12T20:14:24.774768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.779200Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.779200Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:5239716548d7c121cfc3c5ca9a6b0135aa0c316e49ba804a595865741210cd89","observation_id":"a3e698a2-fd82-4342-8bc4-41543c31df8d","resolution":{"observed_at":"2026-08-12T20:14:24.779200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.783626Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.783626Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:572a74147df76f10c141e438aa6311d316b7166b11c274c09020f33742eb03d4","observation_id":"e96ed687-e805-492c-93ee-886693db1e5e","resolution":{"observed_at":"2026-08-12T20:14:24.783626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.01535","last_updated":"2020-02-04T21:02:11Z","snapshot_observed_at":"2026-08-06T22:50:38.500329Z","submitted_at":"2020-02-04T21:02:11Z","title":"Lightweight Convolutional Representations for On-Device Natural Language Processing","version":1},"cited_work":{"arxiv_id":"2002.01535","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.01535","snapshot_observed_at":"2026-08-12T20:14:28.076591Z","title":"Lightweight Convolutional Representations for On-Device Natural Language Processing","venue":"cs.CL","work_id":"3d73e40e-9760-44f7-adc2-29fd6c5d0b3a","year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.788243Z"},"links":{"cited_paper":"/paper/2002.01535","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:29cc27e9ddf8d4488db3f1b8776f72b9e2982df46e7f4e04750f7a27178a1196","observation_id":"f3e32d19-b154-4e06-8d23-daf6b7ef403d","resolution":{"observed_at":"2026-08-12T20:14:28.080938Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.793049Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.793049Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:2e1da07473fd35697474447bab96b6c4f198bdb43bd10bb59ae5b197b6935f9c","observation_id":"130f49ad-de38-4a0f-b525-7d42ef90b7ee","resolution":{"observed_at":"2026-08-12T20:14:24.793049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-12T20:14:24.797643Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.797643Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:e0da59145300ff6076138778bbd5f385863f007e24f4a6b0c1df126fcfa1aeea","observation_id":"1e7a0664-5de4-47d6-9c83-104e17de5b7c","resolution":{"observed_at":"2026-08-12T20:14:24.797643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2211.31344","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:28.046473Z","title":null,"venue":null,"work_id":"2facb6e2-00a0-49a5-8ffc-3c20e9511c24","year":2017},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.803028Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:37dfa9d9f1ee3481cd5778325dae22213466b50adf1763391eed08ce4449718b","observation_id":"d985ed68-6a46-46f6-9ee1-b3d3a12005d1","resolution":{"observed_at":"2026-08-12T20:14:28.051984Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.807564Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.807564Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:066babd8da38a4b15fbb9837cf39fac19337edf2a08563dbb19dd3dc4e0bb88d","observation_id":"0dcbf7d6-ed46-4ec3-9429-864e74e3bf90","resolution":{"observed_at":"2026-08-12T20:14:24.807564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.811995Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.811995Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:153b6af76a8f2b938c31ebd93a8572bb51b6f4d6e2fdb25dfa5aa4ea627fee1a","observation_id":"78664bee-6f86-4786-8330-8d17e56ee497","resolution":{"observed_at":"2026-08-12T20:14:24.811995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.816820Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.816820Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:c21b630595e1816e4abdfaacdae2b93d65b14bceae7b49391c025d6a663949cc","observation_id":"36a6b583-ba00-4d6e-8bda-cc5e0a15457b","resolution":{"observed_at":"2026-08-12T20:14:24.816820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.821294Z","title":null,"venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.821294Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:093501e258241877d35fd66f6519f4c333b3c20b5f9bd6fe034d34ca955005c3","observation_id":"9a96c959-7689-4c83-94a4-3cab0bc894fa","resolution":{"observed_at":"2026-08-12T20:14:24.821294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.825571Z","title":"Lauter, Michael Naehrig, and John Wernsing","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.825571Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:0cf774327a1fa81553dd1cdda40632624e9ac69433510adfba9cb40bbad0f127","observation_id":"af055236-2dfa-4b8d-9b01-3c3f86d99ef8","resolution":{"observed_at":"2026-08-12T20:14:24.825571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.830504Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.830504Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:55dda91d9b36260ff9d6bd866bfd429981f1588bcce23aee6b5416aafee1f049","observation_id":"16e2686c-7a21-4c10-9eb4-0984284a68bf","resolution":{"observed_at":"2026-08-12T20:14:24.830504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.834094Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.834094Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:70a50426c7a2c4a96fa6c92b4af343c74a3c209e0c17d7833d4ce9901f720cca","observation_id":"8e6d014b-228d-43d1-83d9-e39cc0982033","resolution":{"observed_at":"2026-08-12T20:14:24.834094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.838042Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.838042Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:cfb390000ad387f8f009404a569252898b59feac6c024f0ea3f8de1461179262","observation_id":"9cea6395-6948-49bd-a5cd-89463ec30dab","resolution":{"observed_at":"2026-08-12T20:14:24.838042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.841808Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.841808Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:53994215c2b43e0f92f279926b88e530d1de548d40ffde7b1315f45e2b982fdf","observation_id":"0c9a995f-f2bd-40c3-bbd4-347a36acf812","resolution":{"observed_at":"2026-08-12T20:14:24.841808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00969","last_updated":"2020-08-12T20:36:53Z","snapshot_observed_at":"2026-08-11T22:16:14.242580Z","submitted_at":"2018-07-03T04:00:15Z","title":"Confidential Inference via Ternary Model Partitioning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00969","snapshot_observed_at":"2026-08-12T20:14:24.845578Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.845578Z"},"links":{"cited_paper":"/paper/1807.00969","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:baad9b019e7a389ea8310cf175bd5990baf02e6d3498cd9471140e9a36dcfaa9","observation_id":"1e09350b-d344-4c4b-b480-f3ec6ed5d770","resolution":{"observed_at":"2026-08-12T20:14:24.845578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.849721Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.849721Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:88b2b6696bd8e4b2fe52c1b38b62036cbc4adf19990f240c4e29d6de2b9df37e","observation_id":"be11c6b7-710e-48ab-b43c-9284da4387c5","resolution":{"observed_at":"2026-08-12T20:14:24.849721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.857616Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.857616Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:08159e643a148fec522c0c6dcc7a3418152976a9ee28e5614e86450fc1a6af03","observation_id":"dddee61b-b8e5-4b39-a929-af0479b4147a","resolution":{"observed_at":"2026-08-12T20:14:24.857616Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.862114Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.862114Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:c72c584c45ff5e6477111e5904c0b3eff9cc258a378e9e841cf611839e5a1686","observation_id":"aebd2840-6720-4a9e-8468-7f3b344727e6","resolution":{"observed_at":"2026-08-12T20:14:24.862114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.869983Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.869983Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:40eda3d3d4c16acb2a5aef31bd54d1029c09ecec19aba396154387352e58b8a2","observation_id":"754833bc-8c9e-4586-a6a6-c191ae778a2e","resolution":{"observed_at":"2026-08-12T20:14:24.869983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.873955Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.873955Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:0f6330a69e17acc21639c38b106cbc60cd1629e3b0de4d2425393169f4699ce1","observation_id":"8bb21246-4186-436f-99fb-4db158792bf1","resolution":{"observed_at":"2026-08-12T20:14:24.873955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.881024Z","title":"Yu, and Xuyun Zhang","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.881024Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:60d0cc9b3497c5c061a42528b28918e5963f72f4e6f7a01ab360c341d4795198","observation_id":"6c41d576-1722-4f32-98a0-0a7a4966d5bc","resolution":{"observed_at":"2026-08-12T20:14:24.881024Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.884616Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.884616Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:63e039b2b6da83ebe1d0758ad02d29b7317749f26a2df839765da06c17145348","observation_id":"30d81876-abc7-416c-8fa5-a1663615b9be","resolution":{"observed_at":"2026-08-12T20:14:24.884616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.877647Z","title":"In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.877647Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:9e2777cb6326cde6db6d6b08363e3a84cc3a4bf0b72c7b569601f1a7dbad7b25","observation_id":"6d25424d-4000-416c-b284-ccaf23f6dddd","resolution":{"observed_at":"2026-08-12T20:14:24.877647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.892906Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.892906Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7dc099bb3491c976ab094c065f033f73b6db84d1c45cedd585a7946f9cccc3f3","observation_id":"746e166b-c1ed-47ff-ad92-2be7cf827f08","resolution":{"observed_at":"2026-08-12T20:14:24.892906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.11632","last_updated":"2022-05-25T17:59:34Z","snapshot_observed_at":"2026-07-06T09:50:22.512464Z","submitted_at":"2020-08-26T15:43:50Z","title":"GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning","version":2},"cited_work":{"arxiv_id":"2008.11632","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.11632","snapshot_observed_at":"2026-08-12T20:14:27.648896Z","title":"GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning","venue":"cs.CR","work_id":"6ddcaad1-7f21-40d8-88e8-c812f9a03879","year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.897240Z"},"links":{"cited_paper":"/paper/2008.11632","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d58f2cf4324eb044a3e6bc39766ab648faef453836a90d28c7a49f8a27611cbb","observation_id":"aa12fe3b-79f9-48ce-9a3a-ef244e6219f7","resolution":{"observed_at":"2026-08-12T20:14:27.653762Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3620667","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.452321Z","title":null,"venue":null,"work_id":"75d54929-aa48-43a1-aa22-b029a5ce0e8f","year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.888432Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:e973147a72b13429411e10d12259e7e13c9120e6631eedb484f29bd69a536f55","observation_id":"b197a880-88c2-4197-a14a-f3da73ff797c","resolution":{"observed_at":"2026-08-12T20:14:25.456988Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.30884","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:27.561031Z","title":null,"venue":null,"work_id":"d068284d-5a95-4ebb-af13-c61671821b2e","year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.905914Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:ca7a43168781c700db6659ac2151791b4b60fb7dd4c5f1e46e829674447763b7","observation_id":"6d30b0b3-274d-48bf-95a4-3e108b2e05ab","resolution":{"observed_at":"2026-08-12T20:14:27.566618Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.10133","last_updated":"2024-06-17T21:38:42Z","snapshot_observed_at":"2026-08-13T14:02:30.585589Z","submitted_at":"2022-10-18T20:06:06Z","title":"Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware","version":4},"cited_work":{"arxiv_id":"2210.10133","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.10133","snapshot_observed_at":"2026-08-12T20:14:27.488196Z","title":"Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware","venue":"cs.CR","work_id":"290645d5-5639-4c7b-881f-ae028514f0cf","year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.910390Z"},"links":{"cited_paper":"/paper/2210.10133","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:45aece6b7338370581b2fc8d4526812ac81bc183b91dbd3f0d299c88cd161109","observation_id":"ad61212f-8063-4ff1-910a-3c68cd4976db","resolution":{"observed_at":"2026-08-12T20:14:27.492232Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5970.31961","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:27.627781Z","title":"Edward Suh","venue":null,"work_id":"24c49f27-f794-4dba-9833-34c8097ddc00","year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.901891Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7fb919f08f33d360e6144ba1fa276e2f9ff7dd8b5b8b36e3b52e09aaa21696e2","observation_id":"9c50dd22-863a-4f97-bf8d-0adf012412c3","resolution":{"observed_at":"2026-08-12T20:14:27.636964Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.919059Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.919059Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7878b0360cbea6b5a39c4f05d601f27f2101bacbf480f71747a9111d155a2949","observation_id":"2bd4e6db-d930-440e-8c53-a345ec6689ff","resolution":{"observed_at":"2026-08-12T20:14:24.919059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.923057Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.923057Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:93ec054fbfa0a8fdfe8a47f8f60878bcffde657e7c1c7ccea69aba3d0f9e6290","observation_id":"78da335d-8b1b-406c-97b8-3362d19300ec","resolution":{"observed_at":"2026-08-12T20:14:24.923057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.914743Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.914743Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:a69b074a054ab225898486184481a170619b5fb7f15d877de5fb9aad0c160ee9","observation_id":"1cd895ae-1f00-4cf5-9b35-d841f18a8fa0","resolution":{"observed_at":"2026-08-12T20:14:24.914743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.930476Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.930476Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:3f5a9b5ab41b916120d4680115974592c02571a1f39fd548241151a3b919d575","observation_id":"a367a175-5ddc-47ad-8811-db89d8bfe558","resolution":{"observed_at":"2026-08-12T20:14:24.930476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.934477Z","title":"Chandrakasan","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.934477Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:c3e7815cc7d14a9e3c1d6952b84c9e486f56809964996ae772bedf57ba4ea85d","observation_id":"fa02125c-d018-4342-9177-f2a2ef5acc4c","resolution":{"observed_at":"2026-08-12T20:14:24.934477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.926740Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.926740Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:ab40f3013cbfa142abeae5c112fffb80c3fe749fc6b871df03993d2638f45e8c","observation_id":"eb1fd2e1-31c7-420c-9838-3d940c34b129","resolution":{"observed_at":"2026-08-12T20:14:24.926740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.943191Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.943191Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:e79d88ac2773a5536d3e07dc098c69af0a9232733bd2046f8c1e2ffc035a84e2","observation_id":"ea665e22-b3b2-4802-a553-01af658663e3","resolution":{"observed_at":"2026-08-12T20:14:24.943191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05336","last_updated":"2020-06-09T15:17:21Z","snapshot_observed_at":"2026-07-06T09:27:21.949573Z","submitted_at":"2020-06-09T15:17:21Z","title":"On the Effectiveness of Regularization Against Membership Inference Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05336","snapshot_observed_at":"2026-08-12T20:14:24.947478Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.947478Z"},"links":{"cited_paper":"/paper/2006.05336","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:72eef7abbfe6811249ec0373f6cb5ec2d986e47f75bd6e2e5d5ce210c58f5086","observation_id":"728f9154-3e8f-4b9c-9a1c-fe6a2ed3100d","resolution":{"observed_at":"2026-08-12T20:14:24.947478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.939172Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.939172Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:63647271599b42a34c3291e5352fc6884c88ff5676f7e8ed37f1aeceff2ef023","observation_id":"9bc0a84d-7037-4e48-8eae-9906996a4eb5","resolution":{"observed_at":"2026-08-12T20:14:24.939172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.956557Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.956557Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:1f46cb05114faaafe19d11b9ce2183b159cc0668eefc25d510b876479b3f8de5","observation_id":"337f2da7-cdd0-4881-82e1-cba1155f5f37","resolution":{"observed_at":"2026-08-12T20:14:24.956557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.961644Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.961644Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7c6e8740e6e05b2db44933b7e860f7f6195e269f9e09a7acd7bda54fe1e4a38c","observation_id":"2128416f-181f-4d11-8b80-1a287671cb7e","resolution":{"observed_at":"2026-08-12T20:14:24.961644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.952467Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.952467Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:fe0e78695ac9d19a53a80c9c52450c6eba1d41e13d2f5e4b10ea7b2abf9193c7","observation_id":"858abe51-cf85-4c30-b2fb-1446143f1788","resolution":{"observed_at":"2026-08-12T20:14:24.952467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.970877Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.970877Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:eacf04c87be6a1b86dc6d68ac51c0a9b2c5078d8d492bae8691c76f0f706c9a7","observation_id":"03c31983-5997-42ab-885c-d7c2516b4183","resolution":{"observed_at":"2026-08-12T20:14:24.970877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8935.32689","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:27.276649Z","title":null,"venue":null,"work_id":"788096e7-49d9-4713-8d91-0d3d592be857","year":2018},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.975594Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:4545db89d203c8c8eeab7df35f3847ce1496263b7b0bfc4435cde89937cb8a21","observation_id":"ab810b80-d82c-4e02-b0ea-946477018765","resolution":{"observed_at":"2026-08-12T20:14:27.282563Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.966108Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.966108Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:a0324bba31c91c46cb1a4798312f4a9b92ddcba312a9358857cf69db368dbad6","observation_id":"fd256c4e-ce67-4e00-8965-0b139e3bbba8","resolution":{"observed_at":"2026-08-12T20:14:24.966108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.984053Z","title":"Molloy, and Dong Su","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.984053Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:4b7ba3866705acaf2240aa92ab6f14f130893cfbb418798c2a038496319640d3","observation_id":"f7555294-4fef-4dff-a571-ee8e1631f93e","resolution":{"observed_at":"2026-08-12T20:14:24.984053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.987806Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.987806Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:4b0beb1e1a6d3e9cb3571646ee4620310cc414a4f8f80286c379187dc1a5d75a","observation_id":"5b4833bf-c71d-485f-9aa5-ab355128e852","resolution":{"observed_at":"2026-08-12T20:14:24.987806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.979692Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.979692Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:f4eb9dec160a451a4358a875717d3faef9b780c1e4d32b0ff262021edb4b8c15","observation_id":"aa22915e-ba1e-4111-b736-e013ff2efc0c","resolution":{"observed_at":"2026-08-12T20:14:24.979692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.995559Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.995559Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:9c56a56e20b26b17aeb3cbc34f92a0fbd10a4a35f02a6760e0b3ab3aa0de4492","observation_id":"31006564-d1b1-4d7c-8f70-f0ba738b99f3","resolution":{"observed_at":"2026-08-12T20:14:24.995559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.999820Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.999820Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:10a3de9881ad29cb1c0e31847c3da9077954fc64022c64df4cc03e93ec6301f8","observation_id":"7c0cbd72-f705-4463-ac3c-2f8578314ed2","resolution":{"observed_at":"2026-08-12T20:14:24.999820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:24.991458Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:24.991458Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7d961d5ea5275220aa9e47c38bdfeb4a4871da10dc48e0881ec4ef344d4a5894","observation_id":"f10ceab6-d963-4f71-b911-584ba916fe0d","resolution":{"observed_at":"2026-08-12T20:14:24.991458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.008008Z","title":"Zomaya, and Minyi Guo","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.008008Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:fab163effa8dc436f483714894785fe50d6d703d7e0a04749e3de35702cfce83","observation_id":"3a6f5ff7-ef35-4352-88a4-3531ab111517","resolution":{"observed_at":"2026-08-12T20:14:25.008008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.011605Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.011605Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:08e254b67b6ee3f2299950bbb9a844a596000e47d4c0abf9927b9b5bbbc78b86","observation_id":"2b552ec7-1d09-45a0-b2bd-341621e2e7eb","resolution":{"observed_at":"2026-08-12T20:14:25.011605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11121","last_updated":"2024-11-21T02:16:53Z","snapshot_observed_at":"2026-08-13T00:28:32.198769Z","submitted_at":"2024-04-17T07:08:45Z","title":"TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11121","snapshot_observed_at":"2026-08-12T20:14:25.003751Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.003751Z"},"links":{"cited_paper":"/paper/2404.11121","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:3b2bb9744920175c1a04827bc6b41850893948eeeda14913c65864d9eb8263fa","observation_id":"b6b3514c-e7f8-439e-8e7e-dca534b20f04","resolution":{"observed_at":"2026-08-12T20:14:25.003751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.018966Z","title":"Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.018966Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:3679e44425ecedb3e169c8221d697d28b19a77b8e25c8ccaf8abcb632845fb3c","observation_id":"57edd652-09a6-4ed6-99ff-03e73311b3f0","resolution":{"observed_at":"2026-08-12T20:14:25.018966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.022775Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.022775Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:ed89279047b6156a8e9b946e7f294e0faff157abd6f002fa47541a75c4262d9d","observation_id":"ab6708d9-54ab-4a9e-9a84-4080ef2e651b","resolution":{"observed_at":"2026-08-12T20:14:25.022775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.015162Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.015162Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:e100135f5f4bcc78e67766bbf0f3d42e5f07668208a7ed98f28f62a5b4556039","observation_id":"29371ebe-ce0b-4807-a8e7-ad245a8b1e3b","resolution":{"observed_at":"2026-08-12T20:14:25.015162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.034091Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.034091Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d06f75616b168478afed7c135fcc8cc0e4c22c819110b41491d6e7aa130e60c1","observation_id":"d4e164a7-4b63-4cda-9ec0-9f3e2d15b0cd","resolution":{"observed_at":"2026-08-12T20:14:25.034091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.038000Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.038000Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7ab98b105c9ff46fc50058fac673f20f9592682e75b9a8a295220f4942ab8d84","observation_id":"f2ade33a-c858-4c95-926e-20eb7f83617a","resolution":{"observed_at":"2026-08-12T20:14:25.038000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.026561Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.026561Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:6a551d47f63a15a95b62070b650b6cbef783d0ba0ea17a650db67742feb99737","observation_id":"0a3b716e-89dd-455d-bba2-96c798b2fedc","resolution":{"observed_at":"2026-08-12T20:14:25.026561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.030496Z","title":"In 2021 IEEE Symposium on Security and Privacy (SP)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.030496Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:98a464d241fd9cc4f24965a5515b2c3159cba7ee3592f9198239752b537b4ed3","observation_id":"afb90b72-b337-4cf9-8c70-292b5a3706fe","resolution":{"observed_at":"2026-08-12T20:14:25.030496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.049578Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.049578Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:df7a3bd25667bf2fffb24045625d7ba18f8bcd5164d0dd41e1451458aef6218a","observation_id":"7b58f324-080c-4e82-9148-89c7b4bcc25c","resolution":{"observed_at":"2026-08-12T20:14:25.049578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.053164Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.053164Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:c3ed470ee385f3febdb31d8f7b384cfca0ca1f484cebeb9c1da34bb9b4bedc2f","observation_id":"3fdffa5c-b24f-4022-97af-528065c28832","resolution":{"observed_at":"2026-08-12T20:14:25.053164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09489","last_updated":"2023-11-16T01:21:19Z","snapshot_observed_at":"2026-08-13T05:24:16.999683Z","submitted_at":"2023-11-16T01:21:19Z","title":"MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference","version":1},"cited_work":{"arxiv_id":"2311.09489","doi":"10.48550/arxiv.2311.09489","metadata_source":"pith","pith_arxiv_id":"2311.09489","snapshot_observed_at":"2026-08-13T00:16:21.800782Z","title":"MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference","venue":"cs.CR","work_id":"2ca2b590-ed84-4333-9222-e793e530a4bc","year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.041759Z"},"links":{"cited_paper":"/paper/2311.09489","citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7b4e35684b1d8926784aa0abee251ae9f3734002e402dbbd864ba9723e5aa51c","observation_id":"ef0945c2-b8e9-469f-ad05-0ce86dc0c5fd","resolution":{"observed_at":"2026-08-12T20:14:25.431814Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.045814Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.045814Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:b27cded1913a1b29498df9bab10e99520a64c343f443aadc43524dfac34c5532","observation_id":"8af40926-cfff-40ad-b1c4-08ca27e3e583","resolution":{"observed_at":"2026-08-12T20:14:25.045814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.065247Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.065247Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:290be97b0c09c7735a38855df073a911aa378f48eae679ee5b17b52f9d0e2c17","observation_id":"c26bfcfb-b284-4a75-91cb-5c4eb267c5db","resolution":{"observed_at":"2026-08-12T20:14:25.065247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.069971Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.069971Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:a1621979291afc529b56bdbef5386c62fe8ded05718cbd6e5ff1d3d7bad5f7f1","observation_id":"c640dfac-4190-4320-87d0-602ac47b0632","resolution":{"observed_at":"2026-08-12T20:14:25.069971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.057208Z","title":"Rozas, Hisham Shafi, Vedvyas Shanbhogue, and Uday R","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.057208Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:d368f5e80f64b8ac9b113945595b7f6f8db0444a4ca6f410b7763328cf85e68f","observation_id":"0a6e27a4-d7b2-45de-901a-624d3959a7c2","resolution":{"observed_at":"2026-08-12T20:14:25.057208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.060949Z","title":"Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.060949Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:126d7646c3dde37208204cac5f2398bec0903ba2f191b91cf28c5f94d87d8444","observation_id":"b155e48a-63ff-4954-ae5a-e4974c99e62e","resolution":{"observed_at":"2026-08-12T20:14:25.060949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.083162Z","title":"Tullsen, and Hadi Esmaeilzadeh","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.083162Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:0dd2898de3659cfba3f90a7c2d7ae1e368130fe41d4b24f17f10a19eb29baad3","observation_id":"000e036e-459d-40ed-a963-889d5d9ff120","resolution":{"observed_at":"2026-08-12T20:14:25.083162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.086947Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.086947Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:471942060b706bffafd6933ffe8d88740be4a1238ca8a90bf2256f7479e6f8a7","observation_id":"399c3ca5-2309-4477-8f14-352f61f594cb","resolution":{"observed_at":"2026-08-12T20:14:25.086947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.074308Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.074308Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:fc7d95991ea501ca1c1585f26fe76533d245f48fb42a67f9993c096c41eadfcb","observation_id":"babd23f8-c1b0-46d0-8ca7-c6e7d2fe13cf","resolution":{"observed_at":"2026-08-12T20:14:25.074308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.078726Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.078726Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:7762aa8cee0a1c7c77bb46be8edb0b47f3adad607d6e1a5c902214aa739ed225","observation_id":"df093e89-a5fa-401b-abc7-51b3652abe27","resolution":{"observed_at":"2026-08-12T20:14:25.078726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.097576Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.097576Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:b76f20b691a82e54ce07c52b6f3104378669ffdf2a25a410136d39146a22f870","observation_id":"8c5b115d-b633-4a9d-b453-c01c02f9bd15","resolution":{"observed_at":"2026-08-12T20:14:25.097576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.101829Z","title":"Oswald, Flavio D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.101829Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:95f70dfd4afafeaaa0ac38d888aeda9505eba2c38725572c3b8c0a602c75dd86","observation_id":"8516e641-2637-4fc0-ad7f-8031570f320c","resolution":{"observed_at":"2026-08-12T20:14:25.101829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:14:25.090345Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-12T20:14:25.090345Z"},"links":{"citing_paper":"/paper/2411.09945"},"observation_digest":"sha256:504c29f9265d424feece863b9b7aa4c916fa690ca280d3f2480b065e5586774d","observation_id":"be3869f2-f44b-4b81-a73c-900b5e59737a","resolution":{"observed_at":"2026-08-12T20:14:25.090345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.09945","last_updated":"2024-11-15T04:52:11Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-13T14:17:45.721642Z","submitted_at":"2024-11-15T04:52:11Z","title":"TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":2,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":88,"verified_exact":5,"verified_fuzzy":0},"total_outbound_references":168},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 100 of 168 outbound references and 2 inbound Pith citation observations for arXiv:2411.09945."}