{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6ELPMYF57D527BFZP3YXY57U2S","short_pith_number":"pith:6ELPMYF5","schema_version":"1.0","canonical_sha256":"f116f660bdf8fbaf84b97ef17c77f4d4b093e39d896d6db460da33c3ea04fa68","source":{"kind":"arxiv","id":"1705.07663","version":4},"attestation_state":"computed","paper":{"title":"LOGAN: Membership Inference Attacks Against Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Emiliano De Cristofaro, George Danezis, Jamie Hayes, Luca Melis","submitted_at":"2017-05-22T11:05:06Z","abstract_excerpt":"Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator's capacity to learn statistical differences in distributions.\n  We"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1705.07663","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2017-05-22T11:05:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"00881adcd2013d525a682e4c58e650cd2fe886bf8bd8137a3d0cfdc777420214","abstract_canon_sha256":"0418d8d66fb1080480e3a7ca733c252ab39ed4e6ee5c08c630b9443adcf87f98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:43.849143Z","signature_b64":"86C95s2yE3UC99nYuDd8j9u2gHYikFXG2M1aGqI3bq15F+ivzDESsr2iPMADpwxKhaDxqR3qyNKj4ODz0gIkCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f116f660bdf8fbaf84b97ef17c77f4d4b093e39d896d6db460da33c3ea04fa68","last_reissued_at":"2026-05-18T00:07:43.848586Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:43.848586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LOGAN: Membership Inference Attacks Against Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Emiliano De Cristofaro, George Danezis, Jamie Hayes, Luca Melis","submitted_at":"2017-05-22T11:05:06Z","abstract_excerpt":"Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator's capacity to learn statistical differences in distributions.\n  We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07663","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1705.07663","created_at":"2026-05-18T00:07:43.848677+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.07663v4","created_at":"2026-05-18T00:07:43.848677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07663","created_at":"2026-05-18T00:07:43.848677+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ELPMYF57D52","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ELPMYF57D527BFZ","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ELPMYF5","created_at":"2026-05-18T12:31:03.183658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2605.17341","citing_title":"Single-Sample Black-Box Membership Inference Attack against Vision-Language Models via Cross-modal Semantic Alignment","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2506.06057","citing_title":"Hey, That's My Data! Token-Only Dataset Inference in Large Language Models","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2508.11742","citing_title":"Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2510.21783","citing_title":"Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11527","citing_title":"FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S","json":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S.json","graph_json":"https://pith.science/api/pith-number/6ELPMYF57D527BFZP3YXY57U2S/graph.json","events_json":"https://pith.science/api/pith-number/6ELPMYF57D527BFZP3YXY57U2S/events.json","paper":"https://pith.science/paper/6ELPMYF5"},"agent_actions":{"view_html":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S","download_json":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S.json","view_paper":"https://pith.science/paper/6ELPMYF5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.07663&json=true","fetch_graph":"https://pith.science/api/pith-number/6ELPMYF57D527BFZP3YXY57U2S/graph.json","fetch_events":"https://pith.science/api/pith-number/6ELPMYF57D527BFZP3YXY57U2S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S/action/storage_attestation","attest_author":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S/action/author_attestation","sign_citation":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S/action/citation_signature","submit_replication":"https://pith.science/pith/6ELPMYF57D527BFZP3YXY57U2S/action/replication_record"}},"created_at":"2026-05-18T00:07:43.848677+00:00","updated_at":"2026-05-18T00:07:43.848677+00:00"}