{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:L3PATTCW4BNGLDBRWGNFHYBGXG","short_pith_number":"pith:L3PATTCW","schema_version":"1.0","canonical_sha256":"5ede09cc56e05a658c31b19a53e026b99905928230709c1f6a4224826788c31e","source":{"kind":"arxiv","id":"2210.09929","version":3},"attestation_state":"computed","paper":{"title":"Differentially Private Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Arash Vahdat, Karsten Kreis, Tianshi Cao, Tim Dockhorn","submitted_at":"2022-10-18T15:20:47Z","abstract_excerpt":"While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains. Generative models trained with differential privacy (DP) on sensitive data can sidestep this challenge, providing access to synthetic data instead. We build on the recent success of diffusion models (DMs) and introduce Differentially Private Diffusion Models (DPDMs), which enforce privacy using differentially private stochastic gradient descent (DP-SGD). We investigate the DM parameterization and the sampling algorithm, which turn out to be crucial ingredients "},"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":"2210.09929","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-10-18T15:20:47Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"ac6bcda83626891c9de0bb505ae90a4ef87f7de149f1e0f9ca1675d95d130941","abstract_canon_sha256":"c85aab05ce2ab038202d196f69d7e7ded58dc3ee02dcf2f7580e0bcd0feb56e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:29:09.273417Z","signature_b64":"oRqfWcDxNc7uekIEwubqOcKn3eJW50lXRE/LG/YwmnAStnVv473XXq2+3SYcgamDapFG0gZ5dS3+lDGl3Ai4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ede09cc56e05a658c31b19a53e026b99905928230709c1f6a4224826788c31e","last_reissued_at":"2026-07-05T07:29:09.272889Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:29:09.272889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentially Private Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Arash Vahdat, Karsten Kreis, Tianshi Cao, Tim Dockhorn","submitted_at":"2022-10-18T15:20:47Z","abstract_excerpt":"While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains. Generative models trained with differential privacy (DP) on sensitive data can sidestep this challenge, providing access to synthetic data instead. We build on the recent success of diffusion models (DMs) and introduce Differentially Private Diffusion Models (DPDMs), which enforce privacy using differentially private stochastic gradient descent (DP-SGD). We investigate the DM parameterization and the sampling algorithm, which turn out to be crucial ingredients "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.09929","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/2210.09929/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":"2210.09929","created_at":"2026-07-05T07:29:09.272946+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.09929v3","created_at":"2026-07-05T07:29:09.272946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.09929","created_at":"2026-07-05T07:29:09.272946+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3PATTCW4BNG","created_at":"2026-07-05T07:29:09.272946+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3PATTCW4BNGLDBR","created_at":"2026-07-05T07:29:09.272946+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3PATTCW","created_at":"2026-07-05T07:29:09.272946+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09145","citing_title":"PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2601.10237","citing_title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2211.01324","citing_title":"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10439","citing_title":"Filtering Memorization from Parameter-Space in Diffusion Models","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG","json":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG.json","graph_json":"https://pith.science/api/pith-number/L3PATTCW4BNGLDBRWGNFHYBGXG/graph.json","events_json":"https://pith.science/api/pith-number/L3PATTCW4BNGLDBRWGNFHYBGXG/events.json","paper":"https://pith.science/paper/L3PATTCW"},"agent_actions":{"view_html":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG","download_json":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG.json","view_paper":"https://pith.science/paper/L3PATTCW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.09929&json=true","fetch_graph":"https://pith.science/api/pith-number/L3PATTCW4BNGLDBRWGNFHYBGXG/graph.json","fetch_events":"https://pith.science/api/pith-number/L3PATTCW4BNGLDBRWGNFHYBGXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG/action/storage_attestation","attest_author":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG/action/author_attestation","sign_citation":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG/action/citation_signature","submit_replication":"https://pith.science/pith/L3PATTCW4BNGLDBRWGNFHYBGXG/action/replication_record"}},"created_at":"2026-07-05T07:29:09.272946+00:00","updated_at":"2026-07-05T07:29:09.272946+00:00"}