{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OB4RN6X43FNEVDO3FCJKYWKYUA","short_pith_number":"pith:OB4RN6X4","schema_version":"1.0","canonical_sha256":"707916fafcd95a4a8ddb2892ac5958a029d619c9730d51804b7518422f54b1c6","source":{"kind":"arxiv","id":"1910.11567","version":1},"attestation_state":"computed","paper":{"title":"Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Camille Marini, Mathieu N Galtier","submitted_at":"2019-10-25T08:25:03Z","abstract_excerpt":"Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distributed framework for privacy-preserving, traceable and collaborative Machine Learning. Substra gathers data providers and algorithm designers into a network of nodes that can train models on demand but under advanced permission regimes. To guarantee data privacy, Substra implements distributed learning: the data never leave their nodes; only algorithms, predictive models and non-sensitive metadata are exchanged on the"},"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":"1910.11567","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CR","submitted_at":"2019-10-25T08:25:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0863924f955427d2a2cd98b9ba6931ecb4a178b11a9196b324a71bab8f6c5ef6","abstract_canon_sha256":"3ae3e50bf2a271d46487c17eceb3f15df0842ca31f23f611f0a572787924e62b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:14:47.621758Z","signature_b64":"Boqru4uvVpNj2GqfTFMAJNNoOV3yvPkGkkmRtSzuee1q3EWahYlw6w+nXk++sjkyt1BTjf+5y6IKAARpGn+sCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"707916fafcd95a4a8ddb2892ac5958a029d619c9730d51804b7518422f54b1c6","last_reissued_at":"2026-07-05T00:14:47.621368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:14:47.621368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Camille Marini, Mathieu N Galtier","submitted_at":"2019-10-25T08:25:03Z","abstract_excerpt":"Machine learning is promising, but it often needs to process vast amounts of sensitive data which raises concerns about privacy. In this white-paper, we introduce Substra, a distributed framework for privacy-preserving, traceable and collaborative Machine Learning. Substra gathers data providers and algorithm designers into a network of nodes that can train models on demand but under advanced permission regimes. To guarantee data privacy, Substra implements distributed learning: the data never leave their nodes; only algorithms, predictive models and non-sensitive metadata are exchanged on the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.11567","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/1910.11567/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":"1910.11567","created_at":"2026-07-05T00:14:47.621425+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.11567v1","created_at":"2026-07-05T00:14:47.621425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.11567","created_at":"2026-07-05T00:14:47.621425+00:00"},{"alias_kind":"pith_short_12","alias_value":"OB4RN6X43FNE","created_at":"2026-07-05T00:14:47.621425+00:00"},{"alias_kind":"pith_short_16","alias_value":"OB4RN6X43FNEVDO3","created_at":"2026-07-05T00:14:47.621425+00:00"},{"alias_kind":"pith_short_8","alias_value":"OB4RN6X4","created_at":"2026-07-05T00:14:47.621425+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/OB4RN6X43FNEVDO3FCJKYWKYUA","json":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA.json","graph_json":"https://pith.science/api/pith-number/OB4RN6X43FNEVDO3FCJKYWKYUA/graph.json","events_json":"https://pith.science/api/pith-number/OB4RN6X43FNEVDO3FCJKYWKYUA/events.json","paper":"https://pith.science/paper/OB4RN6X4"},"agent_actions":{"view_html":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA","download_json":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA.json","view_paper":"https://pith.science/paper/OB4RN6X4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.11567&json=true","fetch_graph":"https://pith.science/api/pith-number/OB4RN6X43FNEVDO3FCJKYWKYUA/graph.json","fetch_events":"https://pith.science/api/pith-number/OB4RN6X43FNEVDO3FCJKYWKYUA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA/action/storage_attestation","attest_author":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA/action/author_attestation","sign_citation":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA/action/citation_signature","submit_replication":"https://pith.science/pith/OB4RN6X43FNEVDO3FCJKYWKYUA/action/replication_record"}},"created_at":"2026-07-05T00:14:47.621425+00:00","updated_at":"2026-07-05T00:14:47.621425+00:00"}