{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HIBYCSX5RDL66ZMEJY4KRWFBEX","short_pith_number":"pith:HIBYCSX5","schema_version":"1.0","canonical_sha256":"3a03814afd88d7ef65844e38a8d8a125ee6a21c43c45822ce4c94722ee3fd485","source":{"kind":"arxiv","id":"2204.05157","version":1},"attestation_state":"computed","paper":{"title":"SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuong Tran, Ferdinando Fioretto, Keyu Zhu, Pascal Van Hentenryck","submitted_at":"2022-04-11T14:42:54Z","abstract_excerpt":"A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the group attributes is essential. However, in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of the individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to ena"},"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":"2204.05157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-04-11T14:42:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e79bc8742c9743a048e6784eaa26ab2578ac6d530febe32c0fbbfadaab3a20c0","abstract_canon_sha256":"1fcd6089acb62a15fca9719e1f1180a242e1830cbcd4a510db5dcaffbcf354ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:13:17.993625Z","signature_b64":"MZ6lm9kQNoJAGDnb/zAUwg0ZKw+jvJoKZK4mFLG7ZlGQFdnSLnvXLISlZZoiWa5yZdt42ADL5xx89I4o/kO5Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a03814afd88d7ef65844e38a8d8a125ee6a21c43c45822ce4c94722ee3fd485","last_reissued_at":"2026-07-05T04:13:17.993115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:13:17.993115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuong Tran, Ferdinando Fioretto, Keyu Zhu, Pascal Van Hentenryck","submitted_at":"2022-04-11T14:42:54Z","abstract_excerpt":"A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the group attributes is essential. However, in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of the individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to ena"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.05157","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/2204.05157/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":"2204.05157","created_at":"2026-07-05T04:13:17.993180+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.05157v1","created_at":"2026-07-05T04:13:17.993180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.05157","created_at":"2026-07-05T04:13:17.993180+00:00"},{"alias_kind":"pith_short_12","alias_value":"HIBYCSX5RDL6","created_at":"2026-07-05T04:13:17.993180+00:00"},{"alias_kind":"pith_short_16","alias_value":"HIBYCSX5RDL66ZME","created_at":"2026-07-05T04:13:17.993180+00:00"},{"alias_kind":"pith_short_8","alias_value":"HIBYCSX5","created_at":"2026-07-05T04:13:17.993180+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/HIBYCSX5RDL66ZMEJY4KRWFBEX","json":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX.json","graph_json":"https://pith.science/api/pith-number/HIBYCSX5RDL66ZMEJY4KRWFBEX/graph.json","events_json":"https://pith.science/api/pith-number/HIBYCSX5RDL66ZMEJY4KRWFBEX/events.json","paper":"https://pith.science/paper/HIBYCSX5"},"agent_actions":{"view_html":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX","download_json":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX.json","view_paper":"https://pith.science/paper/HIBYCSX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.05157&json=true","fetch_graph":"https://pith.science/api/pith-number/HIBYCSX5RDL66ZMEJY4KRWFBEX/graph.json","fetch_events":"https://pith.science/api/pith-number/HIBYCSX5RDL66ZMEJY4KRWFBEX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX/action/storage_attestation","attest_author":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX/action/author_attestation","sign_citation":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX/action/citation_signature","submit_replication":"https://pith.science/pith/HIBYCSX5RDL66ZMEJY4KRWFBEX/action/replication_record"}},"created_at":"2026-07-05T04:13:17.993180+00:00","updated_at":"2026-07-05T04:13:17.993180+00:00"}