{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:PJXPSTH4VXUL6YQ47IQQAF5LBR","short_pith_number":"pith:PJXPSTH4","schema_version":"1.0","canonical_sha256":"7a6ef94cfcade8bf621cfa210017ab0c4541986deb1d3da0e65f0424b5e34a02","source":{"kind":"arxiv","id":"1306.1066","version":5},"attestation_state":"computed","paper":{"title":"Bayesian Differential Privacy through Posterior Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aikaterini Mitrokotsa, and Zuhe Zhang, Benjamin Rubinstein, Blaine Nelson, Christos Dimitrakakis","submitted_at":"2013-06-05T11:38:46Z","abstract_excerpt":"Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the posterior distribution can be used to achieve a desired level of privacy and utility. To do so, we generalise differential privacy to arbitrary dataset metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular datasets. We prove bou"},"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":"1306.1066","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-06-05T11:38:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"86dfe330290d48902f1cd4cfdbe169400ac2cebde4abfc37c09fee10be7c414a","abstract_canon_sha256":"3bcc1f59a5c19bc26d32e50da4894090360b62f0bee793ade4f22c75e5c87466"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:09.180762Z","signature_b64":"22lohj+d0osRAHMxmyGYVaz25xg+jjz/kALL4laQ79uEr/rw72Pxl7eTvETxER/X8Fxy0akV/mXBpMh8/jl/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a6ef94cfcade8bf621cfa210017ab0c4541986deb1d3da0e65f0424b5e34a02","last_reissued_at":"2026-05-18T00:54:09.180286Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:09.180286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Differential Privacy through Posterior Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aikaterini Mitrokotsa, and Zuhe Zhang, Benjamin Rubinstein, Blaine Nelson, Christos Dimitrakakis","submitted_at":"2013-06-05T11:38:46Z","abstract_excerpt":"Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the posterior distribution can be used to achieve a desired level of privacy and utility. To do so, we generalise differential privacy to arbitrary dataset metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular datasets. We prove bou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.1066","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1306.1066","created_at":"2026-05-18T00:54:09.180368+00:00"},{"alias_kind":"arxiv_version","alias_value":"1306.1066v5","created_at":"2026-05-18T00:54:09.180368+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.1066","created_at":"2026-05-18T00:54:09.180368+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJXPSTH4VXUL","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJXPSTH4VXUL6YQ4","created_at":"2026-05-18T12:27:54.935989+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJXPSTH4","created_at":"2026-05-18T12:27:54.935989+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/PJXPSTH4VXUL6YQ47IQQAF5LBR","json":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR.json","graph_json":"https://pith.science/api/pith-number/PJXPSTH4VXUL6YQ47IQQAF5LBR/graph.json","events_json":"https://pith.science/api/pith-number/PJXPSTH4VXUL6YQ47IQQAF5LBR/events.json","paper":"https://pith.science/paper/PJXPSTH4"},"agent_actions":{"view_html":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR","download_json":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR.json","view_paper":"https://pith.science/paper/PJXPSTH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1306.1066&json=true","fetch_graph":"https://pith.science/api/pith-number/PJXPSTH4VXUL6YQ47IQQAF5LBR/graph.json","fetch_events":"https://pith.science/api/pith-number/PJXPSTH4VXUL6YQ47IQQAF5LBR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR/action/storage_attestation","attest_author":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR/action/author_attestation","sign_citation":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR/action/citation_signature","submit_replication":"https://pith.science/pith/PJXPSTH4VXUL6YQ47IQQAF5LBR/action/replication_record"}},"created_at":"2026-05-18T00:54:09.180368+00:00","updated_at":"2026-05-18T00:54:09.180368+00:00"}