{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K6UC3N3SR6T746PSG3OCVULSEM","short_pith_number":"pith:K6UC3N3S","schema_version":"1.0","canonical_sha256":"57a82db7728fa7fe79f236dc2ad172230e81b2a0f0a14b130f53e85078834bfe","source":{"kind":"arxiv","id":"2205.06469","version":1},"attestation_state":"computed","paper":{"title":"l-Leaks: Membership Inference Attacks with Logits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Shuhao Li, Yajie Wang, Yu-an Tan, Yuanzhang Li","submitted_at":"2022-05-13T06:59:09Z","abstract_excerpt":"Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, especially Membership Inference Attacks (MIAs), the objective of which is to infer the model's training data. So far, most of the membership inference attacks against ML classifiers leverage the shadow model with the same structure as the target model. However, empirical results show that these attacks can be easily mitigated if the shadow model is not clear about the network structure of the target model.\n  In this pape"},"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":"2205.06469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-13T06:59:09Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"f30de9e95acb00136838cc4e41ba74ac333af81929ff5ed9c152e05d99caaf61","abstract_canon_sha256":"3b4c1972805e595a2030236cb898102f3701f7717e66bcfa4b4e399ca232fdde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:58.195960Z","signature_b64":"TxAhx6e43qzhU/9+hPn82If4OfQOUDzyv3Hy05Z6iQkcIIxirm8UHHkTRxVn5NiucP8cHQsRAp0ZySfUYyaOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57a82db7728fa7fe79f236dc2ad172230e81b2a0f0a14b130f53e85078834bfe","last_reissued_at":"2026-07-05T04:22:58.195475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:58.195475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"l-Leaks: Membership Inference Attacks with Logits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Shuhao Li, Yajie Wang, Yu-an Tan, Yuanzhang Li","submitted_at":"2022-05-13T06:59:09Z","abstract_excerpt":"Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, especially Membership Inference Attacks (MIAs), the objective of which is to infer the model's training data. So far, most of the membership inference attacks against ML classifiers leverage the shadow model with the same structure as the target model. However, empirical results show that these attacks can be easily mitigated if the shadow model is not clear about the network structure of the target model.\n  In this pape"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.06469","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.06469/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2205.06469","created_at":"2026-07-05T04:22:58.195540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.06469v1","created_at":"2026-07-05T04:22:58.195540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.06469","created_at":"2026-07-05T04:22:58.195540+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6UC3N3SR6T7","created_at":"2026-07-05T04:22:58.195540+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6UC3N3SR6T746PS","created_at":"2026-07-05T04:22:58.195540+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6UC3N3S","created_at":"2026-07-05T04:22:58.195540+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.05743","citing_title":"When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM","json":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM.json","graph_json":"https://pith.science/api/pith-number/K6UC3N3SR6T746PSG3OCVULSEM/graph.json","events_json":"https://pith.science/api/pith-number/K6UC3N3SR6T746PSG3OCVULSEM/events.json","paper":"https://pith.science/paper/K6UC3N3S"},"agent_actions":{"view_html":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM","download_json":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM.json","view_paper":"https://pith.science/paper/K6UC3N3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.06469&json=true","fetch_graph":"https://pith.science/api/pith-number/K6UC3N3SR6T746PSG3OCVULSEM/graph.json","fetch_events":"https://pith.science/api/pith-number/K6UC3N3SR6T746PSG3OCVULSEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM/action/storage_attestation","attest_author":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM/action/author_attestation","sign_citation":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM/action/citation_signature","submit_replication":"https://pith.science/pith/K6UC3N3SR6T746PSG3OCVULSEM/action/replication_record"}},"created_at":"2026-07-05T04:22:58.195540+00:00","updated_at":"2026-07-05T04:22:58.195540+00:00"}