{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TANPNUCOE7MB3XKDVOPJZWIK2B","short_pith_number":"pith:TANPNUCO","schema_version":"1.0","canonical_sha256":"981af6d04e27d81ddd43ab9e9cd90ad06dd2b2956f52b4441e3ee522cdb307f3","source":{"kind":"arxiv","id":"2507.04457","version":1},"attestation_state":"computed","paper":{"title":"UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Li Xiong, Ruixuan Liu","submitted_at":"2025-07-06T16:35:48Z","abstract_excerpt":"Differentially private (DP) optimization has been widely adopted as a standard approach to provide rigorous privacy guarantees for training datasets. DP auditing verifies whether a model trained with DP optimization satisfies its claimed privacy level by estimating empirical privacy lower bounds through hypothesis testing. Recent O(1) frameworks improve auditing efficiency by checking the membership status of multiple audit samples in a single run, rather than checking individual samples across multiple runs. However, we reveal that there is no free lunch for this improved efficiency: data dep"},"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":"2507.04457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-07-06T16:35:48Z","cross_cats_sorted":[],"title_canon_sha256":"51212d1ec749f58584ff79302cd93b356a531aea0fa1f04124f046074df2c5b5","abstract_canon_sha256":"e9d7e79d9b5d6212d1931e7d0acbc9a12dd41c9cc6d1c480e5f9a83f9a6b2d3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:48.445666Z","signature_b64":"Z9gFziMbuU200nj3yzQ9AE4jml2ducaFnaMnPtMEituJ43Rm/EEMILXLppWq5SXB09ucDXTh1NVq6APXX++ODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"981af6d04e27d81ddd43ab9e9cd90ad06dd2b2956f52b4441e3ee522cdb307f3","last_reissued_at":"2026-07-05T11:32:48.445142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:48.445142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Li Xiong, Ruixuan Liu","submitted_at":"2025-07-06T16:35:48Z","abstract_excerpt":"Differentially private (DP) optimization has been widely adopted as a standard approach to provide rigorous privacy guarantees for training datasets. DP auditing verifies whether a model trained with DP optimization satisfies its claimed privacy level by estimating empirical privacy lower bounds through hypothesis testing. Recent O(1) frameworks improve auditing efficiency by checking the membership status of multiple audit samples in a single run, rather than checking individual samples across multiple runs. However, we reveal that there is no free lunch for this improved efficiency: data dep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.04457","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/2507.04457/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":"2507.04457","created_at":"2026-07-05T11:32:48.445195+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.04457v1","created_at":"2026-07-05T11:32:48.445195+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.04457","created_at":"2026-07-05T11:32:48.445195+00:00"},{"alias_kind":"pith_short_12","alias_value":"TANPNUCOE7MB","created_at":"2026-07-05T11:32:48.445195+00:00"},{"alias_kind":"pith_short_16","alias_value":"TANPNUCOE7MB3XKD","created_at":"2026-07-05T11:32:48.445195+00:00"},{"alias_kind":"pith_short_8","alias_value":"TANPNUCO","created_at":"2026-07-05T11:32:48.445195+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/TANPNUCOE7MB3XKDVOPJZWIK2B","json":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B.json","graph_json":"https://pith.science/api/pith-number/TANPNUCOE7MB3XKDVOPJZWIK2B/graph.json","events_json":"https://pith.science/api/pith-number/TANPNUCOE7MB3XKDVOPJZWIK2B/events.json","paper":"https://pith.science/paper/TANPNUCO"},"agent_actions":{"view_html":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B","download_json":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B.json","view_paper":"https://pith.science/paper/TANPNUCO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.04457&json=true","fetch_graph":"https://pith.science/api/pith-number/TANPNUCOE7MB3XKDVOPJZWIK2B/graph.json","fetch_events":"https://pith.science/api/pith-number/TANPNUCOE7MB3XKDVOPJZWIK2B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B/action/storage_attestation","attest_author":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B/action/author_attestation","sign_citation":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B/action/citation_signature","submit_replication":"https://pith.science/pith/TANPNUCOE7MB3XKDVOPJZWIK2B/action/replication_record"}},"created_at":"2026-07-05T11:32:48.445195+00:00","updated_at":"2026-07-05T11:32:48.445195+00:00"}