{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FJU56GH6Q4F66J7ENSILCBU6AX","short_pith_number":"pith:FJU56GH6","schema_version":"1.0","canonical_sha256":"2a69df18fe870bef27e46c90b1069e05cc60e0c3fe7279fbda5610836189a913","source":{"kind":"arxiv","id":"2109.12062","version":3},"attestation_state":"computed","paper":{"title":"SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Alberto Archetti, Eugenio Lomurno, Leonardo Di Perna, Lorenzo Cazzella, Matteo Matteucci, Stefano Samele","submitted_at":"2021-09-24T16:36:19Z","abstract_excerpt":"Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inferen"},"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":"2109.12062","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-24T16:36:19Z","cross_cats_sorted":["cs.AI","cs.CR","cs.DC"],"title_canon_sha256":"5fd693a6bfcc80df75fd77b651256b6def8a6264c533ddf4cf86db971f0cd3d7","abstract_canon_sha256":"5ded83b2f469961be34c4ace8141a87e31b4d14b7b69921f79a9b33e54e9de39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:55:36.768131Z","signature_b64":"jpfhpfGCIlcBMZJGi14Zj/DBPg7Sjb0tjqEeT5jI5Jwu/DNHIGyT+5vc/LrbMvPAOyb+yljLZ/OpwY7ak+nSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a69df18fe870bef27e46c90b1069e05cc60e0c3fe7279fbda5610836189a913","last_reissued_at":"2026-07-05T04:55:36.767631Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:55:36.767631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.DC"],"primary_cat":"cs.LG","authors_text":"Alberto Archetti, Eugenio Lomurno, Leonardo Di Perna, Lorenzo Cazzella, Matteo Matteucci, Stefano Samele","submitted_at":"2021-09-24T16:36:19Z","abstract_excerpt":"Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inferen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.12062","kind":"arxiv","version":3},"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/2109.12062/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":"2109.12062","created_at":"2026-07-05T04:55:36.767702+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.12062v3","created_at":"2026-07-05T04:55:36.767702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.12062","created_at":"2026-07-05T04:55:36.767702+00:00"},{"alias_kind":"pith_short_12","alias_value":"FJU56GH6Q4F6","created_at":"2026-07-05T04:55:36.767702+00:00"},{"alias_kind":"pith_short_16","alias_value":"FJU56GH6Q4F66J7E","created_at":"2026-07-05T04:55:36.767702+00:00"},{"alias_kind":"pith_short_8","alias_value":"FJU56GH6","created_at":"2026-07-05T04:55:36.767702+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/FJU56GH6Q4F66J7ENSILCBU6AX","json":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX.json","graph_json":"https://pith.science/api/pith-number/FJU56GH6Q4F66J7ENSILCBU6AX/graph.json","events_json":"https://pith.science/api/pith-number/FJU56GH6Q4F66J7ENSILCBU6AX/events.json","paper":"https://pith.science/paper/FJU56GH6"},"agent_actions":{"view_html":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX","download_json":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX.json","view_paper":"https://pith.science/paper/FJU56GH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.12062&json=true","fetch_graph":"https://pith.science/api/pith-number/FJU56GH6Q4F66J7ENSILCBU6AX/graph.json","fetch_events":"https://pith.science/api/pith-number/FJU56GH6Q4F66J7ENSILCBU6AX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX/action/storage_attestation","attest_author":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX/action/author_attestation","sign_citation":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX/action/citation_signature","submit_replication":"https://pith.science/pith/FJU56GH6Q4F66J7ENSILCBU6AX/action/replication_record"}},"created_at":"2026-07-05T04:55:36.767702+00:00","updated_at":"2026-07-05T04:55:36.767702+00:00"}