{"as_of":"2026-08-08T12:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a452918e030fade6e88fdc95094d4e07092cb8a79c83d91db557bfba48e46390","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:29:52.573951Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.03207/citation-record","integrity":"/paper/2506.03207/integrity","json":"/paper/2506.03207/citation-record.json","paper":"/paper/2506.03207"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:55.182217Z","title":null,"venue":null,"work_id":"d5c349dd-db86-44d1-8756-d71fccf9fc56","year":2020},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:50.839710Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:c4594796b633359b63293a5cc9c3d1b6209d9f75eac64b56922ad058fd5f6b23","observation_id":"853aa0c5-9cd6-4158-8815-be7ff8d4ea3d","resolution":{"observed_at":"2026-08-07T11:29:55.187112Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:29:50.920368Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:50.920368Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:088aab679e155db724696e1da2a453baf197884650ca2253b023d0bfb0ebf8ab","observation_id":"0873f8e8-0e80-48bb-9ce1-814ffd7f9087","resolution":{"observed_at":"2026-08-07T11:29:50.920368Z","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":"2026-08-07T11:29:51.067774Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.067774Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:0d39ce456f5fc100bffdc4d149eab5ec23db15ff5888dd884b82a980ed4e5e87","observation_id":"3a5ae0ab-20df-4b07-ab1e-e023059091a3","resolution":{"observed_at":"2026-08-07T11:29:51.067774Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:55.151082Z","title":null,"venue":null,"work_id":"e5ee2f75-adb8-40a8-8caa-56100f4a6c8b","year":1995},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.178397Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:e8f6fd59e3bdb212a70e84ff0e8c9cc84b711c890ca0c923c59846d76e9d21be","observation_id":"2c38923e-86a1-4b74-b0a8-6349ec621c4d","resolution":{"observed_at":"2026-08-07T11:29:55.157114Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:55.137765Z","title":null,"venue":null,"work_id":"fa58c6af-5d6d-4d10-9e0b-25f558ef9f29","year":2021},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.292128Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:2b63f5ddca8c272406992bb55371cd011746e5906178d8fd9afb7c52fa988ef8","observation_id":"bddd2a02-9d13-45e2-9dca-780b2804019a","resolution":{"observed_at":"2026-08-07T11:29:55.142442Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:29:51.403650Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.403650Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:7c2ef4c22cd90909f967cdca0dbfc0f6f59dbf73228fd5e46b06c7e55c4eec3a","observation_id":"55c73354-4ae9-428c-a4d5-4caae4115424","resolution":{"observed_at":"2026-08-07T11:29:51.403650Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:55.056161Z","title":null,"venue":null,"work_id":"16050924-ffaa-4220-9f75-40ba466f64da","year":2025},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.467822Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:ff962efd29128f76cd8fbaf91fd2eddbf8989d19443802dd41784b50f51eb10b","observation_id":"56716b78-aa11-434d-9705-209d5dc724a4","resolution":{"observed_at":"2026-08-07T11:29:55.104166Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.879439Z","title":null,"venue":null,"work_id":"ec3df67b-e4bc-4287-a7dc-7cf1ab7fdd1b","year":2016},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.518530Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:ee43b1032047797424017c3853ce30443b34cd8849b892e9f8910ed8c7db16ae","observation_id":"5e2a112e-134c-4623-b4d9-894f587d340b","resolution":{"observed_at":"2026-08-07T11:29:54.977355Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.715363Z","title":null,"venue":null,"work_id":"79382533-d362-41c7-9fda-495b9bbcb356","year":2024},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.573929Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:277c4be156db912cbda0a7b20a15d3298d9e4718a66af3e18a0ce393b0d92d76","observation_id":"55259669-f86f-427a-a69b-12c71d44ffac","resolution":{"observed_at":"2026-08-07T11:29:54.797750Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.597065Z","title":null,"venue":null,"work_id":"a6c57070-12d5-4191-b605-8f35aca4cf9a","year":null},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.643016Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:32cbff5bf8b2899f1aa82c5bd4dc74a73c551b5d33b942317877749d86c14553","observation_id":"ab05d543-9cb7-472e-a1ec-b890d6290850","resolution":{"observed_at":"2026-08-07T11:29:54.640830Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.423638Z","title":null,"venue":null,"work_id":"f4449ed3-8bc1-4af3-9f5e-5cabd2da8b22","year":2021},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.760615Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:ee076cb55e2061388d8eea8fe474cf14b392cb77920b938de4c7c48d543ecb59","observation_id":"186f14b9-b833-45aa-8c26-c718dd07f16a","resolution":{"observed_at":"2026-08-07T11:29:54.488130Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:29:51.817865Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.817865Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:f4810e25d668d87b69e166afe3cc5b316674fd47aee86fa296df1c4e01ba164d","observation_id":"0718df81-e3d7-4e18-98b4-67d8176900af","resolution":{"observed_at":"2026-08-07T11:29:51.817865Z","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":"2026-08-07T11:29:51.868801Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.868801Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:2b453ba557942b7e75d35bdcc1de799ca094ac8dd1fe96cb9792eb229c048456","observation_id":"4395567a-21ab-4e24-af72-afc504f7ef47","resolution":{"observed_at":"2026-08-07T11:29:51.868801Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.173235Z","title":null,"venue":null,"work_id":"be5fd5e8-80aa-4819-93a3-633f658b5003","year":2016},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.024212Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:236a7238be3c88d5d34166cadaec54e163e21458584b4a0954904289d0250b79","observation_id":"a6da5f83-a4ea-4ef9-b86c-37d310936c79","resolution":{"observed_at":"2026-08-07T11:29:54.209113Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.06421","last_updated":"2020-03-16T18:24:47Z","snapshot_observed_at":"2026-07-06T07:33:44.696417Z","submitted_at":"2019-02-18T06:45:58Z","title":"Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.06421","snapshot_observed_at":"2026-08-07T11:29:52.077619Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.077619Z"},"links":{"cited_paper":"/paper/1902.06421","citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:5aebbc759be8f691b63094e05b58d4394c0b172fefe905bf191700c884da714a","observation_id":"0ce2ed61-fea7-4cd3-8783-948e61d9ddbc","resolution":{"observed_at":"2026-08-07T11:29:52.077619Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.088134Z","title":null,"venue":null,"work_id":"2db3b4ef-ba8d-43dd-b901-28455b7f2baa","year":2025},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.158708Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:9f324de3a1f8f756f6fd89bf16aa5992b53f800a9db745d04fa9b3c43391db22","observation_id":"d80f7cfc-6273-44d0-8d4b-8a6ee65fe501","resolution":{"observed_at":"2026-08-07T11:29:54.099254Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:29:52.210756Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.210756Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:ed334592caac007627061dc36a281b721a714a9aed4ebbaa31f64bb73b9faa32","observation_id":"de611a22-0319-4905-9870-6fcb82585051","resolution":{"observed_at":"2026-08-07T11:29:52.210756Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:53.917382Z","title":null,"venue":null,"work_id":"bd2bad12-6fd7-4065-9462-8aecc535a38b","year":2017},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.262097Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:30238e2c47c573ac9c05c521c8fb17810bc781b9dfe0b58dee2df35c6ea3a93c","observation_id":"bbd3a6c7-b825-4d49-98fc-50e1540e8a21","resolution":{"observed_at":"2026-08-07T11:29:54.057193Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:53.623279Z","title":null,"venue":null,"work_id":"2938bd98-8bb1-4948-bb80-641a3410d92c","year":2020},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.317941Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:2d3b459b08ba7883912c126635f77aa1617b4dafbbb85185bd84b2e6f45a6643","observation_id":"1f2e808f-b6a7-4096-af72-b26f8b5916a0","resolution":{"observed_at":"2026-08-07T11:29:53.770179Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12572","last_updated":"2024-09-19T08:49:40Z","snapshot_observed_at":"2026-07-06T19:18:04.618039Z","submitted_at":"2024-09-19T08:49:40Z","title":"Scalable and Robust Mobile Activity Fingerprinting via Over-the-Air Control Channel in 5G Networks","version":1},"cited_work":{"arxiv_id":"2409.12572","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.12572","snapshot_observed_at":"2026-08-07T11:29:52.667568Z","title":"Scalable and Robust Mobile Activity Fingerprinting via Over-the-Air Control Channel in 5G Networks","venue":"cs.NI","work_id":"07bce8ea-6365-4c11-a675-7fcf557c694b","year":2024},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.371123Z"},"links":{"cited_paper":"/paper/2409.12572","citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:10c93ceeba9036305c5392b8969c15418588ca277c05dbac3138d9975fec5e0e","observation_id":"f26c88a9-3767-414f-95b0-b2ea13214bc3","resolution":{"observed_at":"2026-08-07T11:29:52.694342Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:53.273203Z","title":null,"venue":null,"work_id":"4964093e-34f9-42b3-8dfe-6bbb2efd61cb","year":2024},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.430106Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:f8ee530ca067801ca3a3384ef0fb7afffd3063f32511357ad425a67832a10e37","observation_id":"4a727ce1-55d2-4d12-87a4-0df647319e20","resolution":{"observed_at":"2026-08-07T11:29:53.455837Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:52.921130Z","title":null,"venue":null,"work_id":"bcf3c1aa-2dc3-4972-bffd-d3d5ca51f7f1","year":2024},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.500203Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:84edaac5c858fc41227887e0a250f5d08fdc79c301ee8e932c33bd94de9f0456","observation_id":"b8d42832-1265-437f-97bd-8927d3a97895","resolution":{"observed_at":"2026-08-07T11:29:53.110547Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:52.714738Z","title":null,"venue":null,"work_id":"7cbef69f-c20b-4184-980f-cac59d617069","year":2024},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:52.573951Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:9fbf7766a4db5e1851a79286cc626cff69316bc93dae5d0cc9bbdd7888cc22d3","observation_id":"7a103bf4-243b-4a0d-a984-9c753920fabd","resolution":{"observed_at":"2026-08-07T11:29:52.780166Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.273587Z","title":"In 2019 IEEE symposium on security and privacy (SP)","venue":null,"work_id":"f76933d9-5017-4d43-8eb1-b386ac94e205","year":2019},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.962080Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:9a6d45136ab9a1b70f020419157f093f9a889aa757aa0635dc342003268e7525","observation_id":"088407e2-cf9c-4786-90cb-2a2940a9da0b","resolution":{"observed_at":"2026-08-07T11:29:54.333688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:29:54.561966Z","title":"In IEEE INFOCOM 2020-IEEE conference on computer communications","venue":null,"work_id":"35def9d5-7bb0-4d02-88a7-e68a93e3c6c0","year":2020},"citing_paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:51.708040Z"},"links":{"citing_paper":"/paper/2506.03207"},"observation_digest":"sha256:9f0f69e6ce8601f5654a86fa2675d08599fc1a7a0742a445c524ab4c0e1b9a81","observation_id":"7fdefa1a-a39b-42b2-972a-c254b8baef3b","resolution":{"observed_at":"2026-08-07T11:29:54.590530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.03207","last_updated":"2025-06-02T21:37:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T11:24:49.773765Z","submitted_at":"2025-06-02T21:37:20Z","title":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":25},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.03207."}