{"as_of":"2026-08-07T10:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d40939f37bd6b034c630aff5e5a31a4f0d076f97599bfd7cbe949fe40819a434","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:22:55.084553Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2508.18671/citation-record","integrity":"/paper/2508.18671/integrity","json":"/paper/2508.18671/citation-record.json","paper":"/paper/2508.18671"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:22:55.444351Z","title":"B., MIRONOV, I., T ALWAR, K., AND ZHANG , L","venue":null,"work_id":"727e2783-af90-46a3-9393-aeb01368b643","year":2016},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.960268Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:d8ae2b438f5b9c6e46d21986ec24d4f7eec8bd53d3280b4664e5396edb51c013","observation_id":"9f663c6a-eaac-4a1c-8d29-36176958e021","resolution":{"observed_at":"2026-08-05T16:22:55.447955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.434315Z","title":"Evaluations of machine learning privacy defenses are misleading","venue":null,"work_id":"cfac7be5-b976-49a8-a7e5-1ec4cb74a671","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.965923Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:dcb1ae8ec9b62fe0cbef3800df76915c186d143fc3f4a956c201a8b06302945c","observation_id":"d85d257f-c034-4d36-8eec-acaef26bb55e","resolution":{"observed_at":"2026-08-05T16:22:55.437918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.423880Z","title":"Evaluations of machine learning privacy defenses are misleading","venue":null,"work_id":"6fb3488b-67a1-4e15-8ad5-8ca2db3ee4b0","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.970701Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:63693564c7338cf69216549c66eab98cad7cc8cee8409f5cb37918a04f6878ec","observation_id":"0d217bcb-c3ba-4c3b-99dc-c4cb96aabc8b","resolution":{"observed_at":"2026-08-05T16:22:55.427423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.413792Z","title":"A., JIA, H., T RAVERS , A., Z HANG , B., L IE, D., AND PAPERNOT , N","venue":null,"work_id":"ec6ef298-4896-411d-a9a4-34196a157158","year":2021},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.976232Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:9f16058d2cd0c6530c7a20b394e12fc2a4cec936dbdc70bfbdfa5631a2406e17","observation_id":"c3a4f816-19d5-4bf5-8af0-28b4dcfd3d2c","resolution":{"observed_at":"2026-08-05T16:22:55.417182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.403339Z","title":"California consumer privacy act of 2018, 2018","venue":null,"work_id":"144a0a47-c795-41fb-bab2-d8f5fd043430","year":2018},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.980898Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:d2e626dde4f04aa16b0d5f9de85d10d32f5276f3936babcff71c1bee431ed978","observation_id":"d24e1ba0-89ad-4ee5-8b23-4294d1b8940c","resolution":{"observed_at":"2026-08-05T16:22:55.407021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.392836Z","title":"Towards making systems forget with machine unlearning","venue":null,"work_id":"db594d7d-b71d-460c-ad0f-09048f33890f","year":2015},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.986311Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:d2d558142eeca6a6c025badeeb6a17a250c3ad9e28413e73bea91c4eee5dd1a8","observation_id":"09de7c6a-cbf2-45ed-9825-46bb64044b34","resolution":{"observed_at":"2026-08-05T16:22:55.396622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.382760Z","title":"Membership inference attacks from first principles","venue":null,"work_id":"300bd016-4c18-4e1e-8797-76cbe4ae6992","year":2022},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.991270Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:6bd0ba2ad12db80976a430651b9fb28ffbe2d4a581faa0718cb8a6a550f05bef","observation_id":"b1ed5bc0-2441-4d6c-af48-4b961ae0383f","resolution":{"observed_at":"2026-08-05T16:22:55.386515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.372803Z","title":"The privacy onion effect: Memorization is relative","venue":null,"work_id":"b143dc36-adcb-40e3-b12b-31150a419831","year":2022},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:54.996329Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:099984071de098256084d9e71b6037264976e5eea4f8a4d38ffcc70678a66123","observation_id":"491bc8cd-4c77-4d4b-8efb-7c9dea59077a","resolution":{"observed_at":"2026-08-05T16:22:55.376331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.362531Z","title":"When machine unlearning jeopardizes privacy","venue":null,"work_id":"5e10a54d-0786-4de8-b4d8-572f9ffd644d","year":2021},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.000038Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:a02e19407eb36ae86b6c53a242d565ee3d99e856fd891141f6ac54ca621df3bd","observation_id":"4c923587-961c-4ff1-9594-53c0aada1352","resolution":{"observed_at":"2026-08-05T16:22:55.366090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.352961Z","title":"Differential privacy: A survey of results","venue":null,"work_id":"68d2f51f-12dd-470e-b217-aa4d7035efdd","year":2008},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.004292Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:cef0015259cde87f451f51c243dd463bde842311dcd8dbfd8b7f4f8e54620897","observation_id":"06aa997a-34f8-4b90-a622-64eb6cf0f4b3","resolution":{"observed_at":"2026-08-05T16:22:55.356498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.343206Z","title":"The algorithmic foundations of differential privacy","venue":null,"work_id":"b4a2746d-9047-40bb-9731-4f285d2ef593","year":2014},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.008270Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:7d37e2fbb765af0c6f4cc4f9175a7573ec0595b53a061989fa6e9ea2550cbbfd","observation_id":"d33f53b0-cff6-45fa-adff-e5586aa727e8","resolution":{"observed_at":"2026-08-05T16:22:55.346637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.333701Z","title":"Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016","venue":null,"work_id":"eab74b3c-6eb3-43ae-b64a-188180a07b9c","year":2016},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.011869Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:7d8f95d23986874f413ab7ef3042ca017f6ebab5d8c0876b44f5324bb8bccb25","observation_id":"d9e885e9-9310-46fe-a959-fc5ae7f09cbd","resolution":{"observed_at":"2026-08-05T16:22:55.337008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.323725Z","title":"Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation","venue":null,"work_id":"81078d91-f70b-46f8-a386-fe0ee9bbbf12","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.014891Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:db97f6e368f7f35ba8b985816bf9e324ba21530f39d1038ae0de2dde2683dd2b","observation_id":"3142c421-ccd3-4197-b01c-2cc680173c30","resolution":{"observed_at":"2026-08-05T16:22:55.327607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.314502Z","title":"Fisher information as a measure of privacy: Preserving privacy of households with smart meters using batteries","venue":null,"work_id":"424b2235-737e-4133-8381-09c454bd13ae","year":2017},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.018247Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:7d8855ad5e3a3f8f5ad887c7fa40adc1d0d51c7bc20c5c985cdf78d99501f166","observation_id":"e5bc753e-e2bf-4f05-963f-ba92cc13a757","resolution":{"observed_at":"2026-08-05T16:22:55.317824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.304818Z","title":"Fast machine unlearning without retraining through selective synaptic dampening","venue":null,"work_id":"185dd482-0b27-4f52-8d21-faa2f120668e","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.021188Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:d580e5b1f89c8f185a5731dc7439362f0f429db1ce7002519ab1ea909e3af94d","observation_id":"5a838197-f10d-4fad-bead-255c6bb33dfb","resolution":{"observed_at":"2026-08-05T16:22:55.308122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.294892Z","title":"Eternal sunshine of the spotless net: Selective forgetting in deep networks","venue":null,"work_id":"914443c5-235a-4e59-ab77-24c458d118cd","year":2020},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.024400Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:5d28d421d94f42b88849274317c0921d8fc6081a33601964992c98c390e6a189","observation_id":"dbb6ce9f-1660-4fe5-8deb-8d61c7028f07","resolution":{"observed_at":"2026-08-05T16:22:55.298495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.284580Z","title":"Demo: Ft-privacyscore: Personal- ized privacy scoring service for machine learning participation","venue":null,"work_id":"9075cf64-e81a-437a-b441-6a68b0061120","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.028208Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:17b8f3aaaae21086e4c8be44e8c1422c442599d42a1b510075019a07ff663afd","observation_id":"cdbbb6d7-e18c-47f7-8123-c91ab4d4fc6d","resolution":{"observed_at":"2026-08-05T16:22:55.288423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.18365","last_updated":"2025-09-06T19:57:14Z","snapshot_observed_at":"2026-08-06T14:30:45.750838Z","submitted_at":"2025-07-24T12:46:30Z","title":"RecPS: Privacy Risk Scoring for Recommender Systems","version":4},"cited_work":{"arxiv_id":"2507.18365","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.18365","snapshot_observed_at":"2026-08-05T16:22:55.134619Z","title":"RecPS: Privacy Risk Scoring for Recommender Systems","venue":"cs.IR","work_id":"dbcdfe9b-6d3d-47a1-8ec8-d87d7b5459d8","year":2025},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.031398Z"},"links":{"cited_paper":"/paper/2507.18365","citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:f861297c9488b7d9fcb41bcf32ea90d904f668ce4a7cec7d721a879147c0df46","observation_id":"e0dd6449-7f81-4ff5-97c3-2ca36cb2e696","resolution":{"observed_at":"2026-08-05T16:22:55.140664Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.274778Z","title":"Auditing differentially private machine learning: How private is private sgd? Advances in Neural Information Processing Systems 33 (2020), 22205–22216","venue":null,"work_id":"136d27f5-56c0-43bb-b6dc-263fc179877b","year":2020},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.034895Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:46ba8125b80f46ecc4e609c514c3e9eb867ce82baa97752d7a7580cbfa43789b","observation_id":"fef43abc-0ddf-46ab-ba2c-73a3cd0f2c7c","resolution":{"observed_at":"2026-08-05T16:22:55.278410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.265092Z","title":"The composition theorem for differential privacy","venue":null,"work_id":"acbebca1-9e99-429a-98ee-8f6dc744aa1a","year":2015},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.038962Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:0e23b671943b776f745c26c0dc2f0f0e7a7c292f45ca0053a91c8ac942b641c7","observation_id":"f7e68696-5eeb-4a3c-9948-73685f6d11d3","resolution":{"observed_at":"2026-08-05T16:22:55.268516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.255720Z","title":"C., A FROZ , S., M ILLER , B., SHANKAR , V., B ACHWANI , R., J OSEPH , A","venue":null,"work_id":"e6f6b9e1-aa6d-4fe4-a14e-ce09352b2dda","year":2015},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.042196Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:26b0a56846752d71d0f53d5fe81b3dd35c317c0f3855692bd2173437f675fb76","observation_id":"8f262878-c02f-40bc-b05f-71ee373f1add","resolution":{"observed_at":"2026-08-05T16:22:55.259117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.245659Z","title":"Z., AND MALOOF , M","venue":null,"work_id":"c607993f-a592-4388-b238-ded257a168df","year":2006},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.046179Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:3a515e939908b6a4936cceb5459044bc99f6d2dfcd2809033ca332569716951a","observation_id":"5ebb6cd1-d519-42b7-aebd-df22470fb3dc","resolution":{"observed_at":"2026-08-05T16:22:55.249265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.235507Z","title":"M., S ALMAN , H., AND M ˛ ADRY, A","venue":null,"work_id":"adbae16c-69c1-4b22-b979-02d33a8cae72","year":2023},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.049342Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:bf7ac24f8212d101d750a2a400bdc219aab41641feb2a689bd06f8601c63471f","observation_id":"3a3924cc-49f6-466a-ae4b-ae32cfd92da1","resolution":{"observed_at":"2026-08-05T16:22:55.239183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.225017Z","title":"Membership inference attacks against language models via neighbourhood comparison","venue":null,"work_id":"f5990000-b866-4873-802a-7559f23079be","year":2023},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.052820Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:1cd05bc8b339250fb91a3522fdf57f649be67a03ecd5747497ce709ee1cfa7d4","observation_id":"d373d187-d33b-4c33-a395-c07c3bd05d7d","resolution":{"observed_at":"2026-08-05T16:22:55.228751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.213890Z","title":"Tight auditing of differen- tially private machine learning","venue":null,"work_id":"c6442099-7110-4436-8c7b-3b20a1e745b7","year":2023},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.056255Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:c1ca98b483452c80529881257d93f1084a02d607a266f8c3d2a3383908d8c760","observation_id":"8b073716-1e9b-4689-9397-727364fbaf60","resolution":{"observed_at":"2026-08-05T16:22:55.217485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.02299","last_updated":"2024-09-17T11:55:58Z","snapshot_observed_at":"2026-08-02T02:19:01.218373Z","submitted_at":"2022-09-06T08:51:53Z","title":"A Survey of Machine Unlearning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.02299","snapshot_observed_at":"2026-08-05T16:22:55.059845Z","title":"T., H UYNH , T","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.059845Z"},"links":{"cited_paper":"/paper/2209.02299","citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:8fc9526769d953f861f3959e3014f6201a7849dc0f263d4450a7f70d6ae776af","observation_id":"d655cced-50ce-4684-916d-9bd71fc31f0f","resolution":{"observed_at":"2026-08-05T16:22:55.059845Z","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":"2026-08-05T16:22:55.203607Z","title":"Personal Information Protection and Elec- tronic Documents Act, 2000","venue":null,"work_id":"1bedccd2-6c8e-4e05-be3b-7982ecb3e780","year":2000},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.063518Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:019059e210f20270e3f71f98dfb0020a5fd5a30e72ad98e9e47923edc1bf3b49","observation_id":"49f78488-6821-4757-8013-2ac401056277","resolution":{"observed_at":"2026-08-05T16:22:55.207143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.192461Z","title":"Privacy auditing with one (1) training run","venue":null,"work_id":"6d8ec0b9-c77e-4d92-b530-d30426bb4ace","year":2023},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.067036Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:4f1686459ad6f2449688fdfad4c6be2d7e7258d4d80c2674e269d8c8db48308f","observation_id":"a23d3d47-04cb-4b7a-a876-c25100585e1a","resolution":{"observed_at":"2026-08-05T16:22:55.196427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.181454Z","title":"Privacy auditing with one (1) training run","venue":null,"work_id":"537ff167-649c-4671-b43d-8a3eb1a7d799","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.070298Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:866d2d46eba30e839dce130cce72253fae547c7465922273b5f35261b793a7da","observation_id":"7af0df3b-49c3-4254-9fed-89016fc945b0","resolution":{"observed_at":"2026-08-05T16:22:55.185071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12219","last_updated":"2022-03-28T17:10:52Z","snapshot_observed_at":"2026-07-06T12:41:22.635071Z","submitted_at":"2022-02-24T17:31:08Z","title":"Debugging Differential Privacy: A Case Study for Privacy Auditing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.12219","snapshot_observed_at":"2026-08-05T16:22:55.073521Z","title":"Debugging differential privacy: A case study for privacy auditing","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.073521Z"},"links":{"cited_paper":"/paper/2202.12219","citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:9354ad53a13db8088bed614d91ba765e7baf1ce3ff5c81df0da5a4cdf57967f8","observation_id":"dca5b1f3-ef37-47be-b276-e7d16e92a553","resolution":{"observed_at":"2026-08-05T16:22:55.073521Z","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":"2026-08-05T16:22:55.170606Z","title":null,"venue":null,"work_id":"f2ce4f77-06a2-44d0-9c98-997418e72b40","year":2023},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.077650Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:8ce514d6a2b61d345107a01871f3a63632af2ab9853327a4bdb81fbe7b7f03b7","observation_id":"5f3f64d1-a1f1-4d61-a7e3-cc6c00362d93","resolution":{"observed_at":"2026-08-05T16:22:55.174140Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.159547Z","title":"Machine unlearning: Solutions and challenges","venue":null,"work_id":"27b1f0c2-73e0-479a-9bfa-69c907f6ab1e","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.081438Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:e627b21caa671a010175ca686b8f7088564b108cf542b3592e857f9bca6a73f9","observation_id":"792d93bc-84ca-4558-90b3-5651399db3bd","resolution":{"observed_at":"2026-08-05T16:22:55.163422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T16:22:55.148678Z","title":"privacy onion effect","venue":null,"work_id":"eeee8df4-c5a3-45de-9920-7fbf5d2f41a7","year":2024},"citing_paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T16:22:55.084553Z"},"links":{"citing_paper":"/paper/2508.18671"},"observation_digest":"sha256:4ad362027e7ff6ab28bf498737df22743c71214c2844a8915cc13c548d0bfc13","observation_id":"c48833a5-adc5-402c-aaeb-151d2f22a822","resolution":{"observed_at":"2026-08-05T16:22:55.152239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.18671","last_updated":"2025-08-26T04:29:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T16:22:54.356910Z","submitted_at":"2025-08-26T04:29:33Z","title":"Auditing Approximate Machine Unlearning for Differentially Private Models"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":29},"total_outbound_references":33},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2508.18671."}