{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TQ367NI4XONNP5HFKMTQFLJOSY","short_pith_number":"pith:TQ367NI4","schema_version":"1.0","canonical_sha256":"9c37efb51cbb9ad7f4e5532702ad2e9629f6474c9d188b7e30b2c936c0cd117b","source":{"kind":"arxiv","id":"1802.08232","version":3},"attestation_state":"computed","paper":{"title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Chang Liu, Dawn Song, Jernej Kos, Nicholas Carlini, \\'Ulfar Erlingsson","submitted_at":"2018-02-22T18:42:41Z","abstract_excerpt":"This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization.\n  In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. S"},"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":"1802.08232","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-22T18:42:41Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"3c11da805b5159ca9393209132be56bbaeca0345e5ca9235b8efd873caf685df","abstract_canon_sha256":"33113b8a0e96af5f8772b7600cb2d681935742c5052d987dc0d47bfcfdff840a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:36.587691Z","signature_b64":"zrOPUPjrmhWDR3SY47YOcetNVSuBPdkAXM38p/C15WWyfmd4iJkl3s6jJhk2eN2ZYMDTZda4d0ygFZVZZnEbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c37efb51cbb9ad7f4e5532702ad2e9629f6474c9d188b7e30b2c936c0cd117b","last_reissued_at":"2026-05-17T23:40:36.587262Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:36.587262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Chang Liu, Dawn Song, Jernej Kos, Nicholas Carlini, \\'Ulfar Erlingsson","submitted_at":"2018-02-22T18:42:41Z","abstract_excerpt":"This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization.\n  In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08232","kind":"arxiv","version":3},"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":"1802.08232","created_at":"2026-05-17T23:40:36.587321+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08232v3","created_at":"2026-05-17T23:40:36.587321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08232","created_at":"2026-05-17T23:40:36.587321+00:00"},{"alias_kind":"pith_short_12","alias_value":"TQ367NI4XONN","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"TQ367NI4XONNP5HF","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"TQ367NI4","created_at":"2026-05-18T12:32:56.356000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.02956","citing_title":"The FACTS of Technology-Assisted Sensitivity Review","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2501.02407","citing_title":"Towards the Anonymization of the Language Modeling","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY","json":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY.json","graph_json":"https://pith.science/api/pith-number/TQ367NI4XONNP5HFKMTQFLJOSY/graph.json","events_json":"https://pith.science/api/pith-number/TQ367NI4XONNP5HFKMTQFLJOSY/events.json","paper":"https://pith.science/paper/TQ367NI4"},"agent_actions":{"view_html":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY","download_json":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY.json","view_paper":"https://pith.science/paper/TQ367NI4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08232&json=true","fetch_graph":"https://pith.science/api/pith-number/TQ367NI4XONNP5HFKMTQFLJOSY/graph.json","fetch_events":"https://pith.science/api/pith-number/TQ367NI4XONNP5HFKMTQFLJOSY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY/action/storage_attestation","attest_author":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY/action/author_attestation","sign_citation":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY/action/citation_signature","submit_replication":"https://pith.science/pith/TQ367NI4XONNP5HFKMTQFLJOSY/action/replication_record"}},"created_at":"2026-05-17T23:40:36.587321+00:00","updated_at":"2026-05-17T23:40:36.587321+00:00"}