{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RXQ4WKABNGNMDB4KQMSXUNLZ7K","short_pith_number":"pith:RXQ4WKAB","schema_version":"1.0","canonical_sha256":"8de1cb2801699ac1878a83257a3579faa2ed24dcd4f1885497f53f3170f5f066","source":{"kind":"arxiv","id":"2310.15524","version":3},"attestation_state":"computed","paper":{"title":"On the Inherent Privacy Properties of Discrete Denoising Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eleonora Krea\\v{c}i\\'c, Eli Chien, Haoteng Yin, Haoyu Wang, Pan Li, Rongzhe Wei, Vamsi K. Potluru","submitted_at":"2023-10-24T05:07:31Z","abstract_excerpt":"Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical evaluations on these models, there has been a gap in providing a mathematical characterization of their privacy-preserving capabilities. To address this, we present the pioneering theoretical exploration of the privacy preservation inherent in discrete diffusion models (DDMs) for discrete dataset generation. Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for "},"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":"2310.15524","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-24T05:07:31Z","cross_cats_sorted":[],"title_canon_sha256":"6f935d5bd607e96ca114fe54002bc7733c2cd405aaba4c0d4bd0073963f6c648","abstract_canon_sha256":"e59011cc5ce0b90cf9ab51f1e70893b0a881fe169d73bd0bbcf964c8fe3bacfa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:26:14.815415Z","signature_b64":"p74iL8VHH6crw+4yeixoHTDlx+GyqnRgVcYMUMAD7vTYsJy+yCHEkv9U+JIMP9n5LoxZyJ5PhYgQ+F6LRsKABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8de1cb2801699ac1878a83257a3579faa2ed24dcd4f1885497f53f3170f5f066","last_reissued_at":"2026-07-05T08:26:14.814916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:26:14.814916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Inherent Privacy Properties of Discrete Denoising Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eleonora Krea\\v{c}i\\'c, Eli Chien, Haoteng Yin, Haoyu Wang, Pan Li, Rongzhe Wei, Vamsi K. Potluru","submitted_at":"2023-10-24T05:07:31Z","abstract_excerpt":"Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical evaluations on these models, there has been a gap in providing a mathematical characterization of their privacy-preserving capabilities. To address this, we present the pioneering theoretical exploration of the privacy preservation inherent in discrete diffusion models (DDMs) for discrete dataset generation. Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15524","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.15524/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":"2310.15524","created_at":"2026-07-05T08:26:14.814975+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.15524v3","created_at":"2026-07-05T08:26:14.814975+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15524","created_at":"2026-07-05T08:26:14.814975+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXQ4WKABNGNM","created_at":"2026-07-05T08:26:14.814975+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXQ4WKABNGNMDB4K","created_at":"2026-07-05T08:26:14.814975+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXQ4WKAB","created_at":"2026-07-05T08:26:14.814975+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/RXQ4WKABNGNMDB4KQMSXUNLZ7K","json":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K.json","graph_json":"https://pith.science/api/pith-number/RXQ4WKABNGNMDB4KQMSXUNLZ7K/graph.json","events_json":"https://pith.science/api/pith-number/RXQ4WKABNGNMDB4KQMSXUNLZ7K/events.json","paper":"https://pith.science/paper/RXQ4WKAB"},"agent_actions":{"view_html":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K","download_json":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K.json","view_paper":"https://pith.science/paper/RXQ4WKAB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.15524&json=true","fetch_graph":"https://pith.science/api/pith-number/RXQ4WKABNGNMDB4KQMSXUNLZ7K/graph.json","fetch_events":"https://pith.science/api/pith-number/RXQ4WKABNGNMDB4KQMSXUNLZ7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K/action/storage_attestation","attest_author":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K/action/author_attestation","sign_citation":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K/action/citation_signature","submit_replication":"https://pith.science/pith/RXQ4WKABNGNMDB4KQMSXUNLZ7K/action/replication_record"}},"created_at":"2026-07-05T08:26:14.814975+00:00","updated_at":"2026-07-05T08:26:14.814975+00:00"}