{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NCQMAZLHVZABETYGQ2LNDDH7Q6","short_pith_number":"pith:NCQMAZLH","schema_version":"1.0","canonical_sha256":"68a0c06567ae40124f068696d18cff8796bac1b3dd7a41d78cd00dbb6f78dca4","source":{"kind":"arxiv","id":"2403.13682","version":5},"attestation_state":"computed","paper":{"title":"Threats, Attacks, and Defenses in Machine Unlearning: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chen Chen, Huanyi Ye, Kwok-Yan Lam, Yongsen Zheng, Ziyao Liu","submitted_at":"2024-03-20T15:40:18Z","abstract_excerpt":"Machine Unlearning (MU) has recently gained considerable attention due to its potential to achieve Safe AI by removing the influence of specific data from trained Machine Learning (ML) models. This process, known as knowledge removal, addresses AI governance concerns of training data such as quality, sensitivity, copyright restrictions, and obsolescence. This capability is also crucial for ensuring compliance with privacy regulations such as the Right To Be Forgotten (RTBF). Furthermore, effective knowledge removal mitigates the risk of harmful outcomes, safeguarding against biases, misinforma"},"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":"2403.13682","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-03-20T15:40:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f505421e5d42ee1db2e2cad61e3d7f3552cec8d0cd3271e7ced28c16035bc465","abstract_canon_sha256":"08d4b273be679e2f86ea93278cdf26a5c0231a28d7d34b40969d375225d4bc3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:15.945755Z","signature_b64":"OPEzwDpcg9JqPaW/XTW7YG8dvI/WkfbSxPajWE/WriOdN+JztbRDKnzz2kUAKJTYdH9VrjajK7a8mWO+bZxVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68a0c06567ae40124f068696d18cff8796bac1b3dd7a41d78cd00dbb6f78dca4","last_reissued_at":"2026-07-05T10:15:15.945233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:15.945233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Threats, Attacks, and Defenses in Machine Unlearning: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chen Chen, Huanyi Ye, Kwok-Yan Lam, Yongsen Zheng, Ziyao Liu","submitted_at":"2024-03-20T15:40:18Z","abstract_excerpt":"Machine Unlearning (MU) has recently gained considerable attention due to its potential to achieve Safe AI by removing the influence of specific data from trained Machine Learning (ML) models. This process, known as knowledge removal, addresses AI governance concerns of training data such as quality, sensitivity, copyright restrictions, and obsolescence. This capability is also crucial for ensuring compliance with privacy regulations such as the Right To Be Forgotten (RTBF). Furthermore, effective knowledge removal mitigates the risk of harmful outcomes, safeguarding against biases, misinforma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.13682","kind":"arxiv","version":5},"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/2403.13682/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":"2403.13682","created_at":"2026-07-05T10:15:15.945303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.13682v5","created_at":"2026-07-05T10:15:15.945303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.13682","created_at":"2026-07-05T10:15:15.945303+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCQMAZLHVZAB","created_at":"2026-07-05T10:15:15.945303+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCQMAZLHVZABETYG","created_at":"2026-07-05T10:15:15.945303+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCQMAZLH","created_at":"2026-07-05T10:15:15.945303+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.18786","citing_title":"Leveraging Per-Instance Privacy for Machine Unlearning","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6","json":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6.json","graph_json":"https://pith.science/api/pith-number/NCQMAZLHVZABETYGQ2LNDDH7Q6/graph.json","events_json":"https://pith.science/api/pith-number/NCQMAZLHVZABETYGQ2LNDDH7Q6/events.json","paper":"https://pith.science/paper/NCQMAZLH"},"agent_actions":{"view_html":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6","download_json":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6.json","view_paper":"https://pith.science/paper/NCQMAZLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.13682&json=true","fetch_graph":"https://pith.science/api/pith-number/NCQMAZLHVZABETYGQ2LNDDH7Q6/graph.json","fetch_events":"https://pith.science/api/pith-number/NCQMAZLHVZABETYGQ2LNDDH7Q6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6/action/storage_attestation","attest_author":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6/action/author_attestation","sign_citation":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6/action/citation_signature","submit_replication":"https://pith.science/pith/NCQMAZLHVZABETYGQ2LNDDH7Q6/action/replication_record"}},"created_at":"2026-07-05T10:15:15.945303+00:00","updated_at":"2026-07-05T10:15:15.945303+00:00"}