{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ULRLFNFTKYV74VP7VH4C6R3DE7","short_pith_number":"pith:ULRLFNFT","schema_version":"1.0","canonical_sha256":"a2e2b2b4b3562bfe55ffa9f82f476327c871b407388feced7b4af7a74373a7bd","source":{"kind":"arxiv","id":"1711.10677","version":1},"attestation_state":"computed","paper":{"title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Brian Thorne, Giorgio Patrini, Guillaume Smith, Hamish Ivey-Law, Richard Nock, Stephen Hardy, Wilko Henecka","submitted_at":"2017-11-29T04:29:29Z","abstract_excerpt":"Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in enti"},"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":"1711.10677","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-29T04:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"9fba0bb7f30937a2fb8d7da1eb25ad32b94d49c2ba31c881e63c4d09a2b6590d","abstract_canon_sha256":"c724e57b0cf2a5e8c071e007121e13405868b668f8316ea1a4196ca9e8487787"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:17.289927Z","signature_b64":"BaOBakGrImRD9hUjSrbhbi8fyYYmBQD+LC0+b4rT6yxiaPVON+3ddIFbwFPQQUAaHCOfX9lV6MJG08bkWXsLCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2e2b2b4b3562bfe55ffa9f82f476327c871b407388feced7b4af7a74373a7bd","last_reissued_at":"2026-05-18T00:29:17.289077Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:17.289077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Brian Thorne, Giorgio Patrini, Guillaume Smith, Hamish Ivey-Law, Richard Nock, Stephen Hardy, Wilko Henecka","submitted_at":"2017-11-29T04:29:29Z","abstract_excerpt":"Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in enti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10677","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":""},"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":"1711.10677","created_at":"2026-05-18T00:29:17.289196+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.10677v1","created_at":"2026-05-18T00:29:17.289196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10677","created_at":"2026-05-18T00:29:17.289196+00:00"},{"alias_kind":"pith_short_12","alias_value":"ULRLFNFTKYV7","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"ULRLFNFTKYV74VP7","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"ULRLFNFT","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08343","citing_title":"Private Vertical Federated Inference for Time-Series","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24326","citing_title":"X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00970","citing_title":"Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19015","citing_title":"FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07125","citing_title":"Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation","ref_index":101,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7","json":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7.json","graph_json":"https://pith.science/api/pith-number/ULRLFNFTKYV74VP7VH4C6R3DE7/graph.json","events_json":"https://pith.science/api/pith-number/ULRLFNFTKYV74VP7VH4C6R3DE7/events.json","paper":"https://pith.science/paper/ULRLFNFT"},"agent_actions":{"view_html":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7","download_json":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7.json","view_paper":"https://pith.science/paper/ULRLFNFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.10677&json=true","fetch_graph":"https://pith.science/api/pith-number/ULRLFNFTKYV74VP7VH4C6R3DE7/graph.json","fetch_events":"https://pith.science/api/pith-number/ULRLFNFTKYV74VP7VH4C6R3DE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7/action/storage_attestation","attest_author":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7/action/author_attestation","sign_citation":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7/action/citation_signature","submit_replication":"https://pith.science/pith/ULRLFNFTKYV74VP7VH4C6R3DE7/action/replication_record"}},"created_at":"2026-05-18T00:29:17.289196+00:00","updated_at":"2026-05-18T00:29:17.289196+00:00"}