{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2X7NFEMZ7GZCXCAQ5DSGIONSKL","short_pith_number":"pith:2X7NFEMZ","schema_version":"1.0","canonical_sha256":"d5fed29199f9b22b8810e8e46439b252de97913ca054820472658509cb8e0d6c","source":{"kind":"arxiv","id":"2309.02202","version":1},"attestation_state":"computed","paper":{"title":"On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Achraf Azize, Aymen Al Marjani, Debabrota Basu, Marc Jourdan","submitted_at":"2023-09-05T13:07:25Z","abstract_excerpt":"Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting user studies to name a few. Motivated by the data privacy concerns invoked by these applications, we study the problem of BAI with fixed confidence under $\\epsilon$-global Differential Privacy (DP). First, to quantify the cost of privacy, we derive a lower bound on the sample complexity of any $\\delta$-correct BAI algorithm satisfying $\\epsilon$-global DP. Our lower bound suggests the existence of two privacy regimes "},"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":"2309.02202","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-09-05T13:07:25Z","cross_cats_sorted":["cs.CR","cs.LG","math.ST","stat.TH"],"title_canon_sha256":"ea59f3b6e1f05031d60ef3a7eedda6282a7816c5313c6ed977a0c58dd2a112cb","abstract_canon_sha256":"fc34a2dee83ec3a04fc063dedcfc86dbc7af1cf31485e87e7a800cbb89cbd834"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:57.128164Z","signature_b64":"QR7Ro74qmCxvY4lRErLEPzbx2gEEbk+lQzpHH4aMb89T9Al/xM7f0c1Mo8OVsMyKC9jON3mhPAGinUrPK2+8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5fed29199f9b22b8810e8e46439b252de97913ca054820472658509cb8e0d6c","last_reissued_at":"2026-07-05T06:47:57.127657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:57.127657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Achraf Azize, Aymen Al Marjani, Debabrota Basu, Marc Jourdan","submitted_at":"2023-09-05T13:07:25Z","abstract_excerpt":"Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting user studies to name a few. Motivated by the data privacy concerns invoked by these applications, we study the problem of BAI with fixed confidence under $\\epsilon$-global Differential Privacy (DP). First, to quantify the cost of privacy, we derive a lower bound on the sample complexity of any $\\delta$-correct BAI algorithm satisfying $\\epsilon$-global DP. Our lower bound suggests the existence of two privacy regimes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02202","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/2309.02202/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":"2309.02202","created_at":"2026-07-05T06:47:57.127718+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02202v1","created_at":"2026-07-05T06:47:57.127718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02202","created_at":"2026-07-05T06:47:57.127718+00:00"},{"alias_kind":"pith_short_12","alias_value":"2X7NFEMZ7GZC","created_at":"2026-07-05T06:47:57.127718+00:00"},{"alias_kind":"pith_short_16","alias_value":"2X7NFEMZ7GZCXCAQ","created_at":"2026-07-05T06:47:57.127718+00:00"},{"alias_kind":"pith_short_8","alias_value":"2X7NFEMZ","created_at":"2026-07-05T06:47:57.127718+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21524","citing_title":"Prophet Inequalities under Local Differential Privacy","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL","json":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL.json","graph_json":"https://pith.science/api/pith-number/2X7NFEMZ7GZCXCAQ5DSGIONSKL/graph.json","events_json":"https://pith.science/api/pith-number/2X7NFEMZ7GZCXCAQ5DSGIONSKL/events.json","paper":"https://pith.science/paper/2X7NFEMZ"},"agent_actions":{"view_html":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL","download_json":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL.json","view_paper":"https://pith.science/paper/2X7NFEMZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02202&json=true","fetch_graph":"https://pith.science/api/pith-number/2X7NFEMZ7GZCXCAQ5DSGIONSKL/graph.json","fetch_events":"https://pith.science/api/pith-number/2X7NFEMZ7GZCXCAQ5DSGIONSKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL/action/storage_attestation","attest_author":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL/action/author_attestation","sign_citation":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL/action/citation_signature","submit_replication":"https://pith.science/pith/2X7NFEMZ7GZCXCAQ5DSGIONSKL/action/replication_record"}},"created_at":"2026-07-05T06:47:57.127718+00:00","updated_at":"2026-07-05T06:47:57.127718+00:00"}