{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MAQRQXJVGDMSLHNWSMZGPASCFG","short_pith_number":"pith:MAQRQXJV","schema_version":"1.0","canonical_sha256":"6021185d3530d9259db693326782422991e6e1af35d3608e7791e1e18e8a7fa9","source":{"kind":"arxiv","id":"2112.02230","version":2},"attestation_state":"computed","paper":{"title":"SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"N. Asokan, Sebastian Szyller, Vasisht Duddu","submitted_at":"2021-12-04T03:45:49Z","abstract_excerpt":"Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an ML model, risk violating membership privacy. ML model builders need a principled definition of a metric to quantify the membership privacy risk of (a) individual training data records, (b) computed independently of specific MIAs, (c) which assesses susceptibility to different MIAs, (d) can be used for different applications, and (e) efficiently. None of the prior membership privacy risk metrics simultaneously meet a"},"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":"2112.02230","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2021-12-04T03:45:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a5c6233d57636aca8fa2551fd92bdee147d7de32d6547f80085ddc191e90da65","abstract_canon_sha256":"5a916159a9255d46f1990ce3ad3f58f1be3ca1a04f5f13f9c73a0645784ab390"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:54:17.174480Z","signature_b64":"VMgqsV6uiAi5vvVwDyxRdRr5Ofuwvy988vW1/Bhsc3c+tUAZhOMDFJjJV39mf0V9boYY/2LvYZntqydEsFVBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6021185d3530d9259db693326782422991e6e1af35d3608e7791e1e18e8a7fa9","last_reissued_at":"2026-07-05T04:54:17.174047Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:54:17.174047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"N. Asokan, Sebastian Szyller, Vasisht Duddu","submitted_at":"2021-12-04T03:45:49Z","abstract_excerpt":"Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an ML model, risk violating membership privacy. ML model builders need a principled definition of a metric to quantify the membership privacy risk of (a) individual training data records, (b) computed independently of specific MIAs, (c) which assesses susceptibility to different MIAs, (d) can be used for different applications, and (e) efficiently. None of the prior membership privacy risk metrics simultaneously meet a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02230","kind":"arxiv","version":2},"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/2112.02230/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":"2112.02230","created_at":"2026-07-05T04:54:17.174104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.02230v2","created_at":"2026-07-05T04:54:17.174104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02230","created_at":"2026-07-05T04:54:17.174104+00:00"},{"alias_kind":"pith_short_12","alias_value":"MAQRQXJVGDMS","created_at":"2026-07-05T04:54:17.174104+00:00"},{"alias_kind":"pith_short_16","alias_value":"MAQRQXJVGDMSLHNW","created_at":"2026-07-05T04:54:17.174104+00:00"},{"alias_kind":"pith_short_8","alias_value":"MAQRQXJV","created_at":"2026-07-05T04:54:17.174104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14550","citing_title":"Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG","json":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG.json","graph_json":"https://pith.science/api/pith-number/MAQRQXJVGDMSLHNWSMZGPASCFG/graph.json","events_json":"https://pith.science/api/pith-number/MAQRQXJVGDMSLHNWSMZGPASCFG/events.json","paper":"https://pith.science/paper/MAQRQXJV"},"agent_actions":{"view_html":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG","download_json":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG.json","view_paper":"https://pith.science/paper/MAQRQXJV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.02230&json=true","fetch_graph":"https://pith.science/api/pith-number/MAQRQXJVGDMSLHNWSMZGPASCFG/graph.json","fetch_events":"https://pith.science/api/pith-number/MAQRQXJVGDMSLHNWSMZGPASCFG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG/action/storage_attestation","attest_author":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG/action/author_attestation","sign_citation":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG/action/citation_signature","submit_replication":"https://pith.science/pith/MAQRQXJVGDMSLHNWSMZGPASCFG/action/replication_record"}},"created_at":"2026-07-05T04:54:17.174104+00:00","updated_at":"2026-07-05T04:54:17.174104+00:00"}