{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CJFFTG3RUYZQOA7WNIXIAUZHZA","short_pith_number":"pith:CJFFTG3R","schema_version":"1.0","canonical_sha256":"124a599b71a6330703f66a2e805327c814fd58d8cdc866693d5353cc95536d50","source":{"kind":"arxiv","id":"2306.03257","version":1},"attestation_state":"computed","paper":{"title":"Generating Private Synthetic Data with Genetic Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.NE","authors_text":"Giuseppe Vietri, Jingwu Tang, Terrance Liu, Zhiwei Steven Wu","submitted_at":"2023-06-05T21:19:37Z","abstract_excerpt":"We study the problem of efficiently generating differentially private synthetic data that approximate the statistical properties of an underlying sensitive dataset. In recent years, there has been a growing line of work that approaches this problem using first-order optimization techniques. However, such techniques are restricted to optimizing differentiable objectives only, severely limiting the types of analyses that can be conducted. For example, first-order mechanisms have been primarily successful in approximating statistical queries only in the form of marginals for discrete data domains"},"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":"2306.03257","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2023-06-05T21:19:37Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"ee5285c4cd98b827c25116b9c0917094369a4e821febad737bdb6717c699d49d","abstract_canon_sha256":"efdc0e2b0f3a0cea2cec65ea433cb4c4601c6aadede4eaa9b33f893b5036407e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:55.640783Z","signature_b64":"4f3mifukEmcuNVje/6CKyNGQp5A14r8IAT27FgBA1qsuZQNxIbD8UzkHS+q8yxDOCqJz9TmD+e4Xr5+muK87Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"124a599b71a6330703f66a2e805327c814fd58d8cdc866693d5353cc95536d50","last_reissued_at":"2026-07-05T06:17:55.640304Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:55.640304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generating Private Synthetic Data with Genetic Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.NE","authors_text":"Giuseppe Vietri, Jingwu Tang, Terrance Liu, Zhiwei Steven Wu","submitted_at":"2023-06-05T21:19:37Z","abstract_excerpt":"We study the problem of efficiently generating differentially private synthetic data that approximate the statistical properties of an underlying sensitive dataset. In recent years, there has been a growing line of work that approaches this problem using first-order optimization techniques. However, such techniques are restricted to optimizing differentiable objectives only, severely limiting the types of analyses that can be conducted. For example, first-order mechanisms have been primarily successful in approximating statistical queries only in the form of marginals for discrete data domains"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.03257","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/2306.03257/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":"2306.03257","created_at":"2026-07-05T06:17:55.640367+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.03257v1","created_at":"2026-07-05T06:17:55.640367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.03257","created_at":"2026-07-05T06:17:55.640367+00:00"},{"alias_kind":"pith_short_12","alias_value":"CJFFTG3RUYZQ","created_at":"2026-07-05T06:17:55.640367+00:00"},{"alias_kind":"pith_short_16","alias_value":"CJFFTG3RUYZQOA7W","created_at":"2026-07-05T06:17:55.640367+00:00"},{"alias_kind":"pith_short_8","alias_value":"CJFFTG3R","created_at":"2026-07-05T06:17:55.640367+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/CJFFTG3RUYZQOA7WNIXIAUZHZA","json":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA.json","graph_json":"https://pith.science/api/pith-number/CJFFTG3RUYZQOA7WNIXIAUZHZA/graph.json","events_json":"https://pith.science/api/pith-number/CJFFTG3RUYZQOA7WNIXIAUZHZA/events.json","paper":"https://pith.science/paper/CJFFTG3R"},"agent_actions":{"view_html":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA","download_json":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA.json","view_paper":"https://pith.science/paper/CJFFTG3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.03257&json=true","fetch_graph":"https://pith.science/api/pith-number/CJFFTG3RUYZQOA7WNIXIAUZHZA/graph.json","fetch_events":"https://pith.science/api/pith-number/CJFFTG3RUYZQOA7WNIXIAUZHZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA/action/storage_attestation","attest_author":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA/action/author_attestation","sign_citation":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA/action/citation_signature","submit_replication":"https://pith.science/pith/CJFFTG3RUYZQOA7WNIXIAUZHZA/action/replication_record"}},"created_at":"2026-07-05T06:17:55.640367+00:00","updated_at":"2026-07-05T06:17:55.640367+00:00"}