{"as_of":"2026-08-16T10:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ff2e31b6c55afb32d2cada05d801b621e4696d0dd157577dba43deecdc76716","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:54:57.819552Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2411.11144/citation-record","integrity":"/paper/2411.11144/integrity","json":"/paper/2411.11144/citation-record.json","paper":"/paper/2411.11144"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.627758Z","title":"Deep learning with differential privacy,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.627758Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:ef90acfd1e95efd1fef73ce1dbb5a608d6895be4915474e1f4206c33c76fa650","observation_id":"89877994-a8ac-48fb-8275-7192cf126b18","resolution":{"observed_at":"2026-08-12T18:54:57.627758Z","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":"2026-08-12T18:54:57.632852Z","title":"Membership inference attacks from first principles,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.632852Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:037f56465adb124028ae5aaf3499d6f2b699543d1756ecb61d19bd6603bcd192","observation_id":"ae066db7-cff7-46f5-9fc0-0afceb686d12","resolution":{"observed_at":"2026-08-12T18:54:57.632852Z","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":"2026-08-12T18:54:58.525136Z","title":"The secret sharer: Evaluating and testing unintended memorization in neural net- works,","venue":null,"work_id":"bf0fa278-bd58-432d-89c0-121b20589ef2","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.637527Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:f663b3451563940bc1a95f1c43e945e973bd20ab3cdc850b0980669f9da6aa1d","observation_id":"93d1d80a-4a1c-411e-b835-4b46629264ac","resolution":{"observed_at":"2026-08-12T18:54:58.530074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2207.05801","last_updated":"2022-07-12T19:34:47Z","snapshot_observed_at":"2026-08-13T15:05:25.489550Z","submitted_at":"2022-07-12T19:34:47Z","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05801","snapshot_observed_at":"2026-08-12T18:54:57.642124Z","title":"Relaxloss: defending membership infer- ence attacks without losing utility,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.642124Z"},"links":{"cited_paper":"/paper/2207.05801","citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:fddb88b43257634186a46fdd94945e463c946e0546cff33d5783d0377a313b5f","observation_id":"a06cb806-6156-4522-a979-cbdb0961e20f","resolution":{"observed_at":"2026-08-12T18:54:57.642124Z","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":"2026-08-12T18:54:58.509335Z","title":"Practical membership inference attack against collaborative inference in industrial iot,","venue":null,"work_id":"7a55b665-a53e-4245-a58c-2796d28fc64a","year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.647469Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:dbef77973fb5134da23d2441ecd3a02de005645b755fa4a366237cb0b26aba4f","observation_id":"6a41c433-297a-46bc-be70-29062f931204","resolution":{"observed_at":"2026-08-12T18:54:58.514308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.651818Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.651818Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:4fdf320d7646ea6cb8cfeefb651459d0a66424e3cb53d211ca456615e4b73faa","observation_id":"df31b5b6-ed34-4fa5-9876-10aca03a8562","resolution":{"observed_at":"2026-08-12T18:54:57.651818Z","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":"2026-08-12T18:54:58.481913Z","title":"Predicting future earnings changes using machine learning and detailed financial data,","venue":null,"work_id":"476bd92f-b800-4079-8655-659ebaaaaae3","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.656873Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:77597087f26d53d4ef5e6c74c0b1dbccc0aeb9273817792becf575496332344e","observation_id":"e89c699f-44a4-48ec-825c-37b76d3aa8b5","resolution":{"observed_at":"2026-08-12T18:54:58.486878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.466314Z","title":"Exploring simple siamese representation learning,","venue":null,"work_id":"22ed6303-4292-4702-ac5d-4e2a3712b056","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.661418Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:48d4e1f57b17f13a50a44b2913aed5d60544d2259ae2f9a921f4d8bcf329f267","observation_id":"47328948-3030-4117-b0e1-d865c920e35a","resolution":{"observed_at":"2026-08-12T18:54:58.471289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.450798Z","title":"Exploring simple siamese representation learning,","venue":null,"work_id":"a139c437-233d-46a6-bf4e-4393b4bd91b7","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.665968Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:999356bdba2cfb0ae2f6368c707187617430df335df674ed0b89231fdf67ee32","observation_id":"a4e2b470-ec8a-4f24-b678-9bb607cc92d4","resolution":{"observed_at":"2026-08-12T18:54:58.455653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.433225Z","title":"Leveraging adversarial examples to quantify membership information leakage,","venue":null,"work_id":"4aae31ec-6c91-4129-a4dd-35de7e2ba69c","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.670268Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:408f4c4da1ada8155068729fd2bf24219f578504f8c3a912524b7f7bc894a964","observation_id":"49f6c73a-1d56-4be2-898f-13a6341c16b0","resolution":{"observed_at":"2026-08-12T18:54:58.438160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.416991Z","title":"Simcse: Simple contrastive learning of sentence embeddings,","venue":null,"work_id":"f67741cb-1116-410f-b3d6-6e4f9796a76a","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.674723Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:b96d37f0b0ca29c54036ef6d0485385f13b63567225acf93ab1e0ab85976adb0","observation_id":"2c2815ef-c130-4db1-ba6e-e74562490892","resolution":{"observed_at":"2026-08-12T18:54:58.422303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.679019Z","title":"Bootstrap your own latent-a new approach to self-supervised learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.679019Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:5a7b9d5e37b795cacd361006038a75ed38840b5b68b192e4b5a7c469feec15a9","observation_id":"eabbac77-304f-41e2-b1f0-c08fd9e2dd6b","resolution":{"observed_at":"2026-08-12T18:54:57.679019Z","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":"2026-08-12T18:54:57.683262Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.683262Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:f887a60a62b4015f79a54ec6d59dddee66d2d75ba3427c166be80677b990872a","observation_id":"ddcc38e4-b4b7-4d5d-b4bf-4acc945ccd68","resolution":{"observed_at":"2026-08-12T18:54:57.683262Z","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":"2026-08-12T18:54:57.687687Z","title":"Segmentations-leak: Membership inference attacks and defenses in semantic image segmen- tation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.687687Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:5665ed4b7b963c40d4c6eb21e00b52ab355d56f1500c679122ce04a0a77fc3da","observation_id":"1561a008-285d-4bfb-b945-d191e209bd88","resolution":{"observed_at":"2026-08-12T18:54:57.687687Z","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":"2026-08-12T18:54:58.372120Z","title":"Mem- bership inference via backdooring,","venue":null,"work_id":"764c5901-930b-47e9-973c-1e87878bdff6","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.691956Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:6cb0094259628a30041ecce5e5c8f6e0d4e161a2cfacac497e79ea43ac4e51db","observation_id":"15948c6b-1c55-42c8-aafd-b9b5a88a9637","resolution":{"observed_at":"2026-08-12T18:54:58.377191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.356457Z","title":"Practical blind membership inference attack via differential comparisons,","venue":null,"work_id":"3c54e75f-1718-49b4-bc14-cd1570d3ad9f","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.696338Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:21db09e15f9a3ee463c3da74d2f87c9a5af5da35b10b1e54b94124c6c9b18f96","observation_id":"e9e8bd1d-647b-4a07-81f4-5ea76500c662","resolution":{"observed_at":"2026-08-12T18:54:58.361053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.341395Z","title":"Demystifying the membership inference attack,","venue":null,"work_id":"623593f5-c8c4-4d06-8b84-caeecbe63f16","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.700696Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:72f3b11436e5915c0b01e994daa534524087a44979ba6c07d94406146076889c","observation_id":"bfc91a05-0617-486e-8813-61b102b52230","resolution":{"observed_at":"2026-08-12T18:54:58.346178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.327127Z","title":"Memguard: Defending against black-box membership inference attacks via adver- sarial examples,","venue":null,"work_id":"c31a56bc-5e83-4904-a089-d44f741d6ba0","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.705212Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:160348f00878c29ce5256bbaa4849b538f54c8719eb97e9e3bb51823da33cb84","observation_id":"2be8d422-7bf8-4e2a-8674-29ed0b5c95f6","resolution":{"observed_at":"2026-08-12T18:54:58.331780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.312032Z","title":"When does data augmentation help with membership inference attacks?","venue":null,"work_id":"caeeb2e7-e35d-445e-8571-b4c09107df79","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.709235Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:f55490116d3dbb8cf1e5d5b78df124defcd341db3d5345cc220f01aa888b7bd6","observation_id":"07e3ef05-b8dc-4de9-a3a4-4155e99b7645","resolution":{"observed_at":"2026-08-12T18:54:58.317026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2006.05336","last_updated":"2020-06-09T15:17:21Z","snapshot_observed_at":"2026-08-14T13:12:38.498869Z","submitted_at":"2020-06-09T15:17:21Z","title":"On the Effectiveness of Regularization Against Membership Inference Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05336","snapshot_observed_at":"2026-08-12T18:54:57.714174Z","title":"On the effectiveness of reg- ularization against membership inference attacks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.714174Z"},"links":{"cited_paper":"/paper/2006.05336","citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:6e2c4159f82ccff8cab5d8deb0e6600f036127907205011cef90c2dc82f220cf","observation_id":"136e927d-4e51-431e-868d-124e0fb0a82c","resolution":{"observed_at":"2026-08-12T18:54:57.714174Z","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":"2026-08-12T18:54:58.297008Z","title":"Stolen memories: Leveraging model memorization for calibrated {White-Box} membership inference,","venue":null,"work_id":"9022d8e5-3fc9-4ab1-b68a-48580a3b60e3","year":2020},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.719120Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:ab144e137cd5e665cdf1003d671d6d83b99d556bc71a9bbd6f8a58dfe4ca70a8","observation_id":"0956fc74-6bd3-4c9e-8fe6-37f51a54fcc6","resolution":{"observed_at":"2026-08-12T18:54:58.301695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2203.02077","last_updated":"2022-04-25T23:59:03Z","snapshot_observed_at":"2026-08-13T16:29:47.163670Z","submitted_at":"2022-03-04T00:49:42Z","title":"User-Level Membership Inference Attack against Metric Embedding Learning","version":2},"cited_work":{"arxiv_id":"2203.02077","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.02077","snapshot_observed_at":"2026-08-12T18:54:57.984563Z","title":"User-Level Membership Inference Attack against Metric Embedding Learning","venue":"cs.LG","work_id":"f783719a-5382-4d68-ac47-c4f383cadaf3","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.723524Z"},"links":{"cited_paper":"/paper/2203.02077","citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:fb8eae343f84737b6b6642fd781e1bf9e4b50f30a9839ba292e9a6d85d3ec7ee","observation_id":"20493039-ead3-4e65-844d-63aebb348456","resolution":{"observed_at":"2026-08-12T18:54:57.991861Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.728175Z","title":"Membership inference attacks and defenses in classification models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.728175Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:306089882c66b55f8cfe5f9bfbc1a0a7fa4e958c075651c6966eaa595901ba21","observation_id":"8224e8f6-d113-4e30-8f59-7115235c75f4","resolution":{"observed_at":"2026-08-12T18:54:57.728175Z","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":"2026-08-12T18:54:57.732756Z","title":"Membership leakage in label-only exposures,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.732756Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:c3203ef8f17467cacf979fdcc477f805c66ef3ae707f9978c4a9142e94012f4b","observation_id":"c11bd846-c5b7-4d86-8666-5f750bdead7f","resolution":{"observed_at":"2026-08-12T18:54:57.732756Z","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":"2026-08-12T18:54:58.262701Z","title":"Socinf: Membership inference attacks on social media health data with machine learning,","venue":null,"work_id":"05139e2d-103c-46d5-9c74-6436e076e813","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.737404Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:04845dd122a553a8f252bc62a4a8c93d346eeb084fb807c0dea1b3bbceaf66a9","observation_id":"ec6c2e4e-f1b2-423e-9dc4-3b22fd7caf66","resolution":{"observed_at":"2026-08-12T18:54:58.267296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.742193Z","title":"Membership inference attacks by exploiting loss trajectory,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.742193Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:dba42db2dcb21cd440dddec43bab5a71213cc7150fa25ba0c231d69e2ec44239","observation_id":"3157bf97-0ef1-441c-8664-ebfad57eb354","resolution":{"observed_at":"2026-08-12T18:54:57.742193Z","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":"2026-08-12T18:54:57.746843Z","title":"Membership inference attacks by exploiting loss trajectory,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.746843Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:402e81832b87ecbf28a8e76cf148baa2f2a4c4310c88754818b73b679a138876","observation_id":"0212ee6f-7a6e-4e02-a8a0-0f341a156bd0","resolution":{"observed_at":"2026-08-12T18:54:57.746843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04889","last_updated":"2018-02-13T23:05:05Z","snapshot_observed_at":"2026-08-14T19:46:08.923996Z","submitted_at":"2018-02-13T23:05:05Z","title":"Understanding Membership Inferences on Well-Generalized Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04889","snapshot_observed_at":"2026-08-12T18:54:57.751270Z","title":"Understanding membership inferences on well- generalized learning models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.751270Z"},"links":{"cited_paper":"/paper/1802.04889","citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:8e80ff3df83cc68eeff03442903f294520779d07deba92ab997b29f4e6ed3dee","observation_id":"d3abadae-3eac-4111-b9df-18e7ccdffe57","resolution":{"observed_at":"2026-08-12T18:54:57.751270Z","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":"2026-08-12T18:54:58.238026Z","title":"The audio auditor: User-level membership inference in internet of things voice services,","venue":null,"work_id":"e9f4b91d-8cd7-4c22-93da-40f8ba693e0b","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.756081Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:54e20eee32cbcbbfca8115e143b463bc142565d77421cde72900d7dcfe8955de","observation_id":"0776587d-7dd4-4b4e-81ad-70c121db7927","resolution":{"observed_at":"2026-08-12T18:54:58.243162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.760696Z","title":"Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.760696Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:68c5f07926042de628ccaf6eed5d8d83d078a120ad114264a052573981ec76eb","observation_id":"9016b324-f22d-4193-b7a4-b93fb98d7f20","resolution":{"observed_at":"2026-08-12T18:54:57.760696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.06145","last_updated":"2017-11-29T11:47:25Z","snapshot_observed_at":"2026-08-14T20:40:18.972494Z","submitted_at":"2017-08-21T10:50:24Z","title":"Knock Knock, Who's There? Membership Inference on Aggregate Location Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.06145","snapshot_observed_at":"2026-08-12T18:54:57.765307Z","title":"Knock knock, who’s there? membership inference on aggregate location data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.765307Z"},"links":{"cited_paper":"/paper/1708.06145","citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:ad867203bbfcbdf8bb6622810302949f3868af41cf3cde1c073764fc0ce19aa7","observation_id":"3e7adc52-b810-465e-8b0a-fa849c488c3f","resolution":{"observed_at":"2026-08-12T18:54:57.765307Z","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":"2026-08-12T18:54:58.212778Z","title":"Differential privacy defenses and sampling attacks for membership inference,","venue":null,"work_id":"c999f807-1ccd-4e79-9f96-e789595f2afb","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.770025Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:60fddd878d795763291c385bc4b0fb0688cab821ed893a7a8c9a2029434c5097","observation_id":"6c828998-a2ad-4331-ab8f-068940d7b34e","resolution":{"observed_at":"2026-08-12T18:54:58.217978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.196218Z","title":"White-box vs black-box: Bayes optimal strategies for membership inference,","venue":null,"work_id":"d99daedf-1b8e-4ce8-807b-9707e9672559","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.774472Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:bd79ea9763535fa253c7e24ff8bce2da419b453c95155ed518a330702d21f126","observation_id":"c55a4481-9804-4298-a098-8cf5b5c7e7c3","resolution":{"observed_at":"2026-08-12T18:54:58.201474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.180159Z","title":"Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,","venue":null,"work_id":"f3fe5fa7-bead-42b8-90e4-4863690d22bc","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.778934Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:874b933c8d7282a7fac192926ad12613cb810bc99c13c7b172f56a78969b398c","observation_id":"cbe1b33f-5d89-415a-95ff-72c4d3e4442b","resolution":{"observed_at":"2026-08-12T18:54:58.185248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.164558Z","title":"Evaluating the vulnerability of end-to-end automatic speech recogni- tion models to membership inference attacks","venue":null,"work_id":"f5715844-511a-481e-b102-a930cf81c948","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.783285Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:6f3a076a01b7ace26bd20b59b7a58724337dadc10e82322bfb933b2ce295932b","observation_id":"700440c6-bd46-45cb-8cf1-6aaac1668e05","resolution":{"observed_at":"2026-08-12T18:54:58.169150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.149961Z","title":"Membership inference attacks against machine learning models,","venue":null,"work_id":"b3d98ac7-7771-4c75-b882-37beeed18df8","year":2017},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.787638Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:12301514fd74315d5e93f21069e1f03fc4f172b2f03f15b654d7b1506b777d15","observation_id":"3c827b07-bb14-4e7e-a061-2e8434c2c7d3","resolution":{"observed_at":"2026-08-12T18:54:58.154913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.135120Z","title":"Systematic evaluation of privacy risks of ma- chine learning models,","venue":null,"work_id":"96e4419b-9260-45b5-8a29-3abb06b06a09","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.792411Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:1347d9c284fee4659cd3f318bec0f7f6681d3c2d9b74f5fb0e89bf97c10c33aa","observation_id":"d1d35f68-41b0-4d42-bf1c-3ab2ba8cf637","resolution":{"observed_at":"2026-08-12T18:54:58.140068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.119918Z","title":"Privacy risks of securing machine learning models against adversarial examples,","venue":null,"work_id":"f58d5f65-d5b2-48ef-8808-709a32505760","year":2019},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.796883Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:8ca769d6fab248406f1256c647b900bc699a3e3ae7c2e7a8a262100d4a80360f","observation_id":"08fb08b1-f422-469c-9303-67293f0b37dd","resolution":{"observed_at":"2026-08-12T18:54:58.124635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:57.801398Z","title":"Unsupervised feature learning via non-parametric instance discrimination,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.801398Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:89952fb9a968dd139b9d0b670dc5d28426ae259161bb4274d37406248ca557fc","observation_id":"aa7582c7-b283-4a34-ab0f-c1a040ec3474","resolution":{"observed_at":"2026-08-12T18:54:57.801398Z","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":"2026-08-12T18:54:57.805955Z","title":"Privacy risk in machine learning: Analyzing the connection to overfitting,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.805955Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:6e4742000cab4791791b1394c1ac5f39fd71f51da3b35c62baa991f3ced9c903","observation_id":"164caadb-57c9-4168-a155-775576c978fd","resolution":{"observed_at":"2026-08-12T18:54:57.805955Z","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":"2026-08-12T18:54:58.084735Z","title":"Label-only membership inference attacks and defenses in semantic segmentation models,","venue":null,"work_id":"e48ad130-dd01-4c28-af33-3874f7231a23","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.810349Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:6568fd7420a60ebcad5abd72f8c39fdc96d16d0fab30d5e0ee5fb742cb975f4c","observation_id":"b6e9ba71-091a-4f54-9219-a3afec45f582","resolution":{"observed_at":"2026-08-12T18:54:58.089604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.068324Z","title":"Membership inference attacks against recommender systems,","venue":null,"work_id":"1558e951-2dc0-4bc8-83f3-2d146012ef47","year":2021},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.814980Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:8a8d45647c175025f423b1a4325c223693db7ae197ca279c86baad8920715f6b","observation_id":"f04a5553-1832-4992-b17a-51e5b210f6b4","resolution":{"observed_at":"2026-08-12T18:54:58.073775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:54:58.050576Z","title":"Membership inference attacks against synthetic health data,","venue":null,"work_id":"e6533b5c-1ef5-4934-b4a7-bf04e57269ee","year":2022},"citing_paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T18:54:57.819552Z"},"links":{"citing_paper":"/paper/2411.11144"},"observation_digest":"sha256:962814fc840232c1588dcb54a7519acddfb62dc593fd3de8d5d042f36f263444","observation_id":"be0123c1-519c-418b-96eb-f03f19f44c66","resolution":{"observed_at":"2026-08-12T18:54:58.056129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2411.11144","last_updated":"2024-11-17T18:25:01Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T07:28:01.220661Z","submitted_at":"2024-11-17T18:25:01Z","title":"CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":25},"total_outbound_references":43},"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 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2411.11144."}