{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KOYTVG5MS6MOENBHGWUPIZCLIB","short_pith_number":"pith:KOYTVG5M","schema_version":"1.0","canonical_sha256":"53b13a9bac9798e2342735a8f4644b406c76f90fc24be02fe9047d77ab94cce5","source":{"kind":"arxiv","id":"2506.10024","version":1},"attestation_state":"computed","paper":{"title":"Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CR","authors_text":"Davide Venditti, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto, Giancarlo A. Xompero","submitted_at":"2025-06-09T17:57:43Z","abstract_excerpt":"Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previous knowledge regarding their training data, our approach aims to exploit the same kind of knowledge in"},"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":"2506.10024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-09T17:57:43Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"554f0086d5f86cc931f168076dd653bc8c93235c32b6e44ae6fb3350909524f1","abstract_canon_sha256":"3e2cce10c0c9bef4324761e775f75f83cc47f15b5eb5035dce043b64c83813c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:58.282804Z","signature_b64":"dDcOwS44orMKO3/wPIgIeoLEJaqU4fw1k0QgPPkbxUmQDFLE6Vr7vQVLkykaRvRMsCw+ZFawvYsb4WItxoVyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53b13a9bac9798e2342735a8f4644b406c76f90fc24be02fe9047d77ab94cce5","last_reissued_at":"2026-07-05T11:56:58.282173Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:58.282173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CR","authors_text":"Davide Venditti, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto, Giancarlo A. Xompero","submitted_at":"2025-06-09T17:57:43Z","abstract_excerpt":"Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previous knowledge regarding their training data, our approach aims to exploit the same kind of knowledge in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10024","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/2506.10024/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":"2506.10024","created_at":"2026-07-05T11:56:58.282240+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.10024v1","created_at":"2026-07-05T11:56:58.282240+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10024","created_at":"2026-07-05T11:56:58.282240+00:00"},{"alias_kind":"pith_short_12","alias_value":"KOYTVG5MS6MO","created_at":"2026-07-05T11:56:58.282240+00:00"},{"alias_kind":"pith_short_16","alias_value":"KOYTVG5MS6MOENBH","created_at":"2026-07-05T11:56:58.282240+00:00"},{"alias_kind":"pith_short_8","alias_value":"KOYTVG5M","created_at":"2026-07-05T11:56:58.282240+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB","json":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB.json","graph_json":"https://pith.science/api/pith-number/KOYTVG5MS6MOENBHGWUPIZCLIB/graph.json","events_json":"https://pith.science/api/pith-number/KOYTVG5MS6MOENBHGWUPIZCLIB/events.json","paper":"https://pith.science/paper/KOYTVG5M"},"agent_actions":{"view_html":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB","download_json":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB.json","view_paper":"https://pith.science/paper/KOYTVG5M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.10024&json=true","fetch_graph":"https://pith.science/api/pith-number/KOYTVG5MS6MOENBHGWUPIZCLIB/graph.json","fetch_events":"https://pith.science/api/pith-number/KOYTVG5MS6MOENBHGWUPIZCLIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB/action/storage_attestation","attest_author":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB/action/author_attestation","sign_citation":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB/action/citation_signature","submit_replication":"https://pith.science/pith/KOYTVG5MS6MOENBHGWUPIZCLIB/action/replication_record"}},"created_at":"2026-07-05T11:56:58.282240+00:00","updated_at":"2026-07-05T11:56:58.282240+00:00"}