{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NEBIMDRQECTNTNNBCTVM5FIZDC","short_pith_number":"pith:NEBIMDRQ","schema_version":"1.0","canonical_sha256":"6902860e3020a6d9b5a114eace951918945c9535b57d8f4c717849e47a16f3a0","source":{"kind":"arxiv","id":"2503.23536","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Unlearnable Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiahao Li, Xiangyuan Lan, Yang Gu, Yiqiang Chen, Yunbing Xing","submitted_at":"2025-03-30T17:41:30Z","abstract_excerpt":"Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security. By introducing perturbations to the training data, ULD degrades model performance, making it difficult for unauthorized models to extract useful representations. Despite the growing significance of ULD, existing surveys predominantly focus on related fields, such as adversarial attacks and machine unlearning, with little attention given to ULD as an independent area of study. This survey fills t"},"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":"2503.23536","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-30T17:41:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8fab2e13231ff346d90c19529c6d9fbdf0730f2616033ba420d8c841a982c077","abstract_canon_sha256":"9609682fa7ba65583cde72bb3fec48c1b68b5d5542f9d702691c5a93512dbfc9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:45.939375Z","signature_b64":"+/dEAMSenmMHdtqUYDRdKUbSUxWuKdXrgGQ1FyWrgSY0I2lkHEvgCdYH9n0b9QKdEgUtos4UM9Icw0Ii3XQwCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6902860e3020a6d9b5a114eace951918945c9535b57d8f4c717849e47a16f3a0","last_reissued_at":"2026-07-05T10:42:45.938801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:45.938801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Unlearnable Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiahao Li, Xiangyuan Lan, Yang Gu, Yiqiang Chen, Yunbing Xing","submitted_at":"2025-03-30T17:41:30Z","abstract_excerpt":"Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security. By introducing perturbations to the training data, ULD degrades model performance, making it difficult for unauthorized models to extract useful representations. Despite the growing significance of ULD, existing surveys predominantly focus on related fields, such as adversarial attacks and machine unlearning, with little attention given to ULD as an independent area of study. This survey fills t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.23536","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/2503.23536/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":"2503.23536","created_at":"2026-07-05T10:42:45.938874+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.23536v2","created_at":"2026-07-05T10:42:45.938874+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.23536","created_at":"2026-07-05T10:42:45.938874+00:00"},{"alias_kind":"pith_short_12","alias_value":"NEBIMDRQECTN","created_at":"2026-07-05T10:42:45.938874+00:00"},{"alias_kind":"pith_short_16","alias_value":"NEBIMDRQECTNTNNB","created_at":"2026-07-05T10:42:45.938874+00:00"},{"alias_kind":"pith_short_8","alias_value":"NEBIMDRQ","created_at":"2026-07-05T10:42:45.938874+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2606.21146","citing_title":"ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19999","citing_title":"LLM Benchmark Datasets Should Be Contamination-Resistant","ref_index":61,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12792","citing_title":"SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC","json":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC.json","graph_json":"https://pith.science/api/pith-number/NEBIMDRQECTNTNNBCTVM5FIZDC/graph.json","events_json":"https://pith.science/api/pith-number/NEBIMDRQECTNTNNBCTVM5FIZDC/events.json","paper":"https://pith.science/paper/NEBIMDRQ"},"agent_actions":{"view_html":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC","download_json":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC.json","view_paper":"https://pith.science/paper/NEBIMDRQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.23536&json=true","fetch_graph":"https://pith.science/api/pith-number/NEBIMDRQECTNTNNBCTVM5FIZDC/graph.json","fetch_events":"https://pith.science/api/pith-number/NEBIMDRQECTNTNNBCTVM5FIZDC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC/action/storage_attestation","attest_author":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC/action/author_attestation","sign_citation":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC/action/citation_signature","submit_replication":"https://pith.science/pith/NEBIMDRQECTNTNNBCTVM5FIZDC/action/replication_record"}},"created_at":"2026-07-05T10:42:45.938874+00:00","updated_at":"2026-07-05T10:42:45.938874+00:00"}