{"as_of":"2026-08-16T22:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d4814f898869256ff184f594d551d7e0e51311bdb1f11a0ec08a20fa717edcd9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:14:24.845578Z","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":"2026-05-24T22:16:24.795487Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.00969","last_updated":"2020-08-12T20:36:53Z","snapshot_observed_at":"2026-08-14T18:56:23.118450Z","submitted_at":"2018-07-03T04:00:15Z","title":"Confidential Inference via Ternary Model Partitioning","version":3},"cited_work":{"arxiv_id":"1807.00969","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1807.00969","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"71c67e54-87d2-4bbf-8244-7b01d6546a78","year":2018},"citing_paper":{"arxiv_id":"1907.06034","last_updated":"2019-07-13T09:17:57Z","snapshot_observed_at":"2026-08-02T07:32:10.003575Z","submitted_at":"2019-07-13T09:17:57Z","title":"Towards Characterizing and Limiting Information Exposure in DNN Layers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T22:15:17.001996Z"},"links":{"cited_paper":"/paper/1807.00969","citing_paper":"/paper/1907.06034"},"observation_digest":"sha256:9d7c4cd216d832f8d8fdafecd7927510884809b307345140b00bdb0420c5d21f","observation_id":"58921d93-d6aa-4bc4-8725-d62e8a0fb525","resolution":{"observed_at":"2026-05-24T22:16:24.798333Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00969","last_updated":"2020-08-12T20:36:53Z","snapshot_observed_at":"2026-08-14T18:56:23.118450Z","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":"2026-08-14T10:34:40.308851Z","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":"sha256:20b752f41b9ba0280dee7c26e58f9424d39737106e8c0df14cf89bc41d593144","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":{"arxiv_id":"1807.00969","last_updated":"2020-08-12T20:36:53Z","snapshot_observed_at":"2026-08-14T18:56:23.118450Z","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":"2026-08-11T12:40:57.022782Z","title":"arXiv preprint arXiv:1807.00969 (2018), 1–14","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13939","last_updated":"2024-12-18T15:21:53Z","snapshot_observed_at":"2026-08-13T19:45:57.495863Z","submitted_at":"2024-12-18T15:21:53Z","title":"Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:57.022782Z"},"links":{"cited_paper":"/paper/1807.00969","citing_paper":"/paper/2412.13939"},"observation_digest":"sha256:6ff6040db3aab507eec39ee426f66d33e3abcbcbab1ca118058e673d5ef369ba","observation_id":"2e4428df-7480-4d33-94c1-3811d134c23f","resolution":{"observed_at":"2026-08-11T12:40:57.022782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1807.00969/citation-record","integrity":"/paper/1807.00969/integrity","json":"/paper/1807.00969/citation-record.json","paper":"/paper/1807.00969"},"outbound":[],"paper":{"arxiv_id":"1807.00969","last_updated":"2020-08-12T20:36:53Z","latest_version":3,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-14T18:56:23.118450Z","submitted_at":"2018-07-03T04:00:15Z","title":"Confidential Inference via Ternary Model Partitioning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1807.00969."}