{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TNLTMG3WLSERORFGTTOR4FVFBD","short_pith_number":"pith:TNLTMG3W","schema_version":"1.0","canonical_sha256":"9b57361b765c891744a69cdd1e16a508d899902e121453ecc4aee48cacc352aa","source":{"kind":"arxiv","id":"2204.07752","version":1},"attestation_state":"computed","paper":{"title":"Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Febrianti Wibawa, Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp, Umit Cali","submitted_at":"2022-04-16T08:38:35Z","abstract_excerpt":"Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica"},"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":"2204.07752","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-04-16T08:38:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1742b2325d4476d40cb9389b967003e0c55d1490d4b04f7ca86b2ddc67d0c0b7","abstract_canon_sha256":"706da553576e1d26b25b543ba8febdc90fb31339eb9745ea4f1240039b1888bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:15:18.162934Z","signature_b64":"FgspBcnUO2VgIeYEDV49Wccw05mFKZNFecQp5qh7rCfUxRnDwgAAQ56MZ6nSChfpxo94zxTQcWGLkNLaI4XuBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b57361b765c891744a69cdd1e16a508d899902e121453ecc4aee48cacc352aa","last_reissued_at":"2026-07-05T04:15:18.162540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:15:18.162540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Febrianti Wibawa, Ferhat Ozgur Catak, Murat Kuzlu, Salih Sarp, Umit Cali","submitted_at":"2022-04-16T08:38:35Z","abstract_excerpt":"Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.07752","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/2204.07752/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":"2204.07752","created_at":"2026-07-05T04:15:18.162596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.07752v1","created_at":"2026-07-05T04:15:18.162596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.07752","created_at":"2026-07-05T04:15:18.162596+00:00"},{"alias_kind":"pith_short_12","alias_value":"TNLTMG3WLSER","created_at":"2026-07-05T04:15:18.162596+00:00"},{"alias_kind":"pith_short_16","alias_value":"TNLTMG3WLSERORFG","created_at":"2026-07-05T04:15:18.162596+00:00"},{"alias_kind":"pith_short_8","alias_value":"TNLTMG3W","created_at":"2026-07-05T04:15:18.162596+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/TNLTMG3WLSERORFGTTOR4FVFBD","json":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD.json","graph_json":"https://pith.science/api/pith-number/TNLTMG3WLSERORFGTTOR4FVFBD/graph.json","events_json":"https://pith.science/api/pith-number/TNLTMG3WLSERORFGTTOR4FVFBD/events.json","paper":"https://pith.science/paper/TNLTMG3W"},"agent_actions":{"view_html":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD","download_json":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD.json","view_paper":"https://pith.science/paper/TNLTMG3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.07752&json=true","fetch_graph":"https://pith.science/api/pith-number/TNLTMG3WLSERORFGTTOR4FVFBD/graph.json","fetch_events":"https://pith.science/api/pith-number/TNLTMG3WLSERORFGTTOR4FVFBD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD/action/storage_attestation","attest_author":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD/action/author_attestation","sign_citation":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD/action/citation_signature","submit_replication":"https://pith.science/pith/TNLTMG3WLSERORFGTTOR4FVFBD/action/replication_record"}},"created_at":"2026-07-05T04:15:18.162596+00:00","updated_at":"2026-07-05T04:15:18.162596+00:00"}