{"as_of":"2026-08-17T08:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7497e0c31421494b30662df52f15bfea95e417a45005bb7850ede8c7116e1beb","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:56:05.925061Z","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":"2026-07-02T20:57:23.312202Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":"2104.08305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-07-02T20:57:23.312202Z","title":"Jagannatha, B","venue":null,"work_id":"ca3a3d0e-0eab-4af9-b6a8-d590ad845f2b","year":2021},"citing_paper":{"arxiv_id":"2310.16789","last_updated":"2024-03-09T22:26:06Z","snapshot_observed_at":"2026-08-08T18:07:29.632928Z","submitted_at":"2023-10-25T17:21:23Z","title":"Detecting Pretraining Data from Large Language Models","version":3},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-17T18:07:24.533329Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2310.16789"},"observation_digest":"sha256:1cadc3b48729b0eca31e613417e05eaa3fae19ca2f6724c6cea2677ec79ceef6","observation_id":"1402fa65-65ff-4970-aa4e-1bdb7a7a0584","resolution":{"observed_at":"2026-05-17T18:07:24.713765Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":"2104.08305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-07-02T20:57:23.312202Z","title":"Jagannatha, B","venue":null,"work_id":"ca3a3d0e-0eab-4af9-b6a8-d590ad845f2b","year":2021},"citing_paper":{"arxiv_id":"2501.02407","last_updated":"2026-05-20T14:04:13Z","snapshot_observed_at":"2026-08-12T13:56:40.611651Z","submitted_at":"2025-01-05T00:03:18Z","title":"Towards the Anonymization of the Language Modeling","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-23T06:28:16.975305Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2501.02407"},"observation_digest":"sha256:c98353f519d6b7a2f4861c74721ed2b5dcf0d4dae08d3a3259a9d0b55a604788","observation_id":"257c0821-d1ef-480c-ae18-cf1290eee821","resolution":{"observed_at":"2026-05-23T06:32:39.513413Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-10T21:47:30.110969Z","title":"Membership inference attack susceptibility of clinical language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03941","last_updated":"2025-01-07T17:02:33Z","snapshot_observed_at":"2026-08-16T04:28:07.369095Z","submitted_at":"2025-01-07T17:02:33Z","title":"Synthetic Data Privacy Metrics","version":1},"reference_index":104,"source":"pdf_text","source_observed_at":"2026-08-10T21:47:30.110969Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2501.03941"},"observation_digest":"sha256:fa5c20d0cdce55382bae6ab70318625334b1fc1d69fafb594dd0cd86e5505a04","observation_id":"75e0d7c1-e896-445e-8955-0c2f71d19b1b","resolution":{"observed_at":"2026-08-10T21:47:30.110969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-16T05:56:05.925061Z","title":"arXiv preprint arXiv:2104.08305 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.21036","last_updated":"2025-05-01T10:10:01Z","snapshot_observed_at":"2026-08-16T05:49:03.725594Z","submitted_at":"2025-04-28T05:34:53Z","title":"Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:56:05.925061Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2504.21036"},"observation_digest":"sha256:707ad37710cdc417ee8e55f6ef863fa170bb5563a116c1c70ca7356d33ca304b","observation_id":"9cc571b6-0d81-468c-a431-cd115d822cc2","resolution":{"observed_at":"2026-08-16T05:56:05.925061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-15T20:16:09.389602Z","title":"Membership inference attack susceptibility of clinical language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13819","last_updated":"2025-05-20T01:58:43Z","snapshot_observed_at":"2026-08-16T20:25:58.624573Z","submitted_at":"2025-05-20T01:58:43Z","title":"Fragments to Facts: Partial-Information Fragment Inference from LLMs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:09.389602Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2505.13819"},"observation_digest":"sha256:5a231f5b35b4200e90905e969ff34c588b6a4716d8e4581a2670303495d9a806","observation_id":"0947d675-f46e-4524-a871-03e07e1a84c8","resolution":{"observed_at":"2026-08-15T20:16:09.389602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-07T04:33:16.912279Z","title":"Membership inference attack suscep- tibility of clinical language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10424","last_updated":"2025-06-12T07:23:56Z","snapshot_observed_at":"2026-08-15T02:34:21.674051Z","submitted_at":"2025-06-12T07:23:56Z","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T04:33:16.912279Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2506.10424"},"observation_digest":"sha256:660898d3f4b2f25b61b9ae4d305c5f44457510bdac11338d2e976732ba5a7562","observation_id":"511de3b9-d4c2-49ca-850f-d28796d01f96","resolution":{"observed_at":"2026-08-07T04:33:16.912279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-07T00:46:10.092761Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.12699","last_updated":"2025-06-19T06:30:24Z","snapshot_observed_at":"2026-08-14T17:40:51.768214Z","submitted_at":"2025-06-15T03:14:03Z","title":"SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T00:46:10.092761Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2506.12699"},"observation_digest":"sha256:cbbf2e0b458022c737146931ed1c036d55553afe8519e25d6150ea38970423b7","observation_id":"3471d20b-b584-48ff-99ca-ce2e80c38b12","resolution":{"observed_at":"2026-08-07T00:46:10.092761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-07T00:30:35.110437Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.13972","last_updated":"2025-07-03T17:45:38Z","snapshot_observed_at":"2026-08-16T22:50:40.168604Z","submitted_at":"2025-06-16T20:22:07Z","title":"Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T00:30:35.110437Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2506.13972"},"observation_digest":"sha256:028f1971fdc750f0dafa46cdebac0b44c485b2c28b0891e6a86e8499fa09e84f","observation_id":"35ea8178-8806-4476-a7c9-97beaec3ca41","resolution":{"observed_at":"2026-08-07T00:30:35.110437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":"2104.08305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-07-02T20:57:23.312202Z","title":"Jagannatha, B","venue":null,"work_id":"ca3a3d0e-0eab-4af9-b6a8-d590ad845f2b","year":2021},"citing_paper":{"arxiv_id":"2507.06056","last_updated":"2026-04-20T04:08:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-08T14:58:28Z","title":"Data Compressibility Quantifies LLM Memorization","version":4},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-19T06:00:12.027708Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2507.06056"},"observation_digest":"sha256:d959786b6c70928aa504e9ea9b11ae5268d27577e46468ae679c4bf8d5eed72d","observation_id":"05d069b2-6601-4db2-a964-fee0a8743362","resolution":{"observed_at":"2026-05-19T06:02:07.682146Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-08-15T18:20:44.386267Z","title":"Membership inference attack susceptibility of clinical language models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.18302","last_updated":"2025-07-24T11:18:27Z","snapshot_observed_at":"2026-08-15T18:13:03.801767Z","submitted_at":"2025-07-24T11:18:27Z","title":"LoRA-Leak: Membership Inference Attacks Against LoRA Fine-tuned Language Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:20:44.386267Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2507.18302"},"observation_digest":"sha256:f431a29217f4da56599dfcbc3ce23b4395b1eb1bcafa30ac39b7fe30f50602e6","observation_id":"6bbfa29f-679f-4868-b35b-45f2fa72831d","resolution":{"observed_at":"2026-08-15T18:20:44.386267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models","version":1},"cited_work":{"arxiv_id":"2104.08305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.08305","snapshot_observed_at":"2026-07-02T20:57:23.312202Z","title":"Jagannatha, B","venue":null,"work_id":"ca3a3d0e-0eab-4af9-b6a8-d590ad845f2b","year":2021},"citing_paper":{"arxiv_id":"2606.07996","last_updated":"2026-06-06T06:27:54Z","snapshot_observed_at":"2026-08-12T17:53:51.469237Z","submitted_at":"2026-06-06T06:27:54Z","title":"MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-27T20:02:50.169589Z"},"links":{"cited_paper":"/paper/2104.08305","citing_paper":"/paper/2606.07996"},"observation_digest":"sha256:b1795b44c2d951b552a28d4e35b1006078f9f72a00462df7add2286c3d23cd55","observation_id":"b94f5828-2308-4ed4-bbc5-0e9a0dc89b8d","resolution":{"observed_at":"2026-07-02T20:57:23.313636Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2104.08305/citation-record","integrity":"/paper/2104.08305/integrity","json":"/paper/2104.08305/citation-record.json","paper":"/paper/2104.08305"},"outbound":[],"paper":{"arxiv_id":"2104.08305","last_updated":"2021-04-16T18:29:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T18:31:00.571216Z","submitted_at":"2021-04-16T18:29:58Z","title":"Membership Inference Attack Susceptibility of Clinical Language Models"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2104.08305."}