{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7FA4OGGWFS3RMTQM3E3F63BREJ","short_pith_number":"pith:7FA4OGGW","schema_version":"1.0","canonical_sha256":"f941c718d62cb7164e0cd9365f6c31227d8bb6e70f71bfbef0f405fcfa7c0e5f","source":{"kind":"arxiv","id":"1703.01106","version":2},"attestation_state":"computed","paper":{"title":"Differentially Private Bayesian Learning on Distributed Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"Antti Honkela, Eemil Lagerspetz, Kana Shimizu, Mikko Heikkil\\\"a, Samuel Kaski, Sasu Tarkoma","submitted_at":"2017-03-03T10:44:47Z","abstract_excerpt":"Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data ho"},"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":"1703.01106","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-03T10:44:47Z","cross_cats_sorted":["cs.CR","cs.LG","stat.CO"],"title_canon_sha256":"bbe0274960ebbae6e6b1a6c6281e70f98c973513baa6a5f7bf3eb49ce3f94ef6","abstract_canon_sha256":"8e8187cd0b06391ebacd895bd559b8996b349c08a83647a70ad82d285fb08877"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:35.175082Z","signature_b64":"z42z82rnm97/Kq6B1eWyL3IbiMsPYC+HspqBwrhyAGo4W+YLvvL9YkD5VCyMPTYI98HTCP/EhLGkWaRKUR+SBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f941c718d62cb7164e0cd9365f6c31227d8bb6e70f71bfbef0f405fcfa7c0e5f","last_reissued_at":"2026-05-18T00:43:35.174694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:35.174694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentially Private Bayesian Learning on Distributed Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"Antti Honkela, Eemil Lagerspetz, Kana Shimizu, Mikko Heikkil\\\"a, Samuel Kaski, Sasu Tarkoma","submitted_at":"2017-03-03T10:44:47Z","abstract_excerpt":"Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data ho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.01106","kind":"arxiv","version":2},"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":"1703.01106","created_at":"2026-05-18T00:43:35.174752+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.01106v2","created_at":"2026-05-18T00:43:35.174752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.01106","created_at":"2026-05-18T00:43:35.174752+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FA4OGGWFS3R","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FA4OGGWFS3RMTQM","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FA4OGGW","created_at":"2026-05-18T12:31:05.417338+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/7FA4OGGWFS3RMTQM3E3F63BREJ","json":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ.json","graph_json":"https://pith.science/api/pith-number/7FA4OGGWFS3RMTQM3E3F63BREJ/graph.json","events_json":"https://pith.science/api/pith-number/7FA4OGGWFS3RMTQM3E3F63BREJ/events.json","paper":"https://pith.science/paper/7FA4OGGW"},"agent_actions":{"view_html":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ","download_json":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ.json","view_paper":"https://pith.science/paper/7FA4OGGW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.01106&json=true","fetch_graph":"https://pith.science/api/pith-number/7FA4OGGWFS3RMTQM3E3F63BREJ/graph.json","fetch_events":"https://pith.science/api/pith-number/7FA4OGGWFS3RMTQM3E3F63BREJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ/action/storage_attestation","attest_author":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ/action/author_attestation","sign_citation":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ/action/citation_signature","submit_replication":"https://pith.science/pith/7FA4OGGWFS3RMTQM3E3F63BREJ/action/replication_record"}},"created_at":"2026-05-18T00:43:35.174752+00:00","updated_at":"2026-05-18T00:43:35.174752+00:00"}