{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2OMS7CNQUFOSBJWFFRML3GKQBA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d589370869f7dbc7560336f974dc2d4e1377a410f06add39e82c2ac0e813a5ce","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-02T16:59:36Z","title_canon_sha256":"a6473c91efe4c9bf696adf8d75d3d46f8d00c517220deefb8f4a1df9eab9522a"},"schema_version":"1.0","source":{"id":"2306.01684","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.01684","created_at":"2026-07-05T07:32:18Z"},{"alias_kind":"arxiv_version","alias_value":"2306.01684v2","created_at":"2026-07-05T07:32:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01684","created_at":"2026-07-05T07:32:18Z"},{"alias_kind":"pith_short_12","alias_value":"2OMS7CNQUFOS","created_at":"2026-07-05T07:32:18Z"},{"alias_kind":"pith_short_16","alias_value":"2OMS7CNQUFOSBJWF","created_at":"2026-07-05T07:32:18Z"},{"alias_kind":"pith_short_8","alias_value":"2OMS7CNQ","created_at":"2026-07-05T07:32:18Z"}],"graph_snapshots":[{"event_id":"sha256:15b82fc1535b9aab87205068c9727d51c448a666c3d049c75dcee07af858052b","target":"graph","created_at":"2026-07-05T07:32:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.01684/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Differentially private training algorithms like DP-SGD protect sensitive training data by ensuring that trained models do not reveal private information. An alternative approach, which this paper studies, is to use a sensitive dataset to generate synthetic data that is differentially private with respect to the original data, and then non-privately training a model on the synthetic data. Doing so has several advantages: synthetic data can be reused for other tasks (including for hyper parameter tuning), retained indefinitely, and shared with third parties without sacrificing privacy. However, ","authors_text":"Alexey Kurakin, Andreas Terzis, Liam MacDermed, Natalia Ponomareva, Umar Syed","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-02T16:59:36Z","title":"Harnessing large-language models to generate private synthetic text"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01684","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:99b303677416965ee3d3361dde0d1e4b193f3a4ebc7da69026980458041c155e","target":"record","created_at":"2026-07-05T07:32:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d589370869f7dbc7560336f974dc2d4e1377a410f06add39e82c2ac0e813a5ce","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-02T16:59:36Z","title_canon_sha256":"a6473c91efe4c9bf696adf8d75d3d46f8d00c517220deefb8f4a1df9eab9522a"},"schema_version":"1.0","source":{"id":"2306.01684","kind":"arxiv","version":2}},"canonical_sha256":"d3992f89b0a15d20a6c52c58bd9950083deb23a973332db8e2e61465989b1b51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3992f89b0a15d20a6c52c58bd9950083deb23a973332db8e2e61465989b1b51","first_computed_at":"2026-07-05T07:32:18.934645Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:32:18.934645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nXvZ4qELKZU25zUDHIf4zYY5rqmu7UtKf95BOcKt2te70/2qQLiHK6pA6nm641ZI4LSxZp9Apo4VO4UUeTO8CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:32:18.936265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.01684","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99b303677416965ee3d3361dde0d1e4b193f3a4ebc7da69026980458041c155e","sha256:15b82fc1535b9aab87205068c9727d51c448a666c3d049c75dcee07af858052b"],"state_sha256":"af32229c8ef6d9e3c45b69edb8389a7bae3f6e8f47b79bedf05ab7e7b23a67f2"}