{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6SPE2ZQYHLLDAUFKZPTMFDGKLO","short_pith_number":"pith:6SPE2ZQY","schema_version":"1.0","canonical_sha256":"f49e4d66183ad63050aacbe6c28cca5ba6b76b6a1119547ae167c8362e836b1f","source":{"kind":"arxiv","id":"1506.00001","version":2},"attestation_state":"computed","paper":{"title":"A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Hong Shen, Zhigang Lu","submitted_at":"2015-05-29T01:20:42Z","abstract_excerpt":"The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, $k$NN attack discloses the target user's sensitive information by creating $k$ fake nearest neighbours by non-sensitive information. Among the current solutions against $k$NN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the gl"},"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":"1506.00001","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2015-05-29T01:20:42Z","cross_cats_sorted":[],"title_canon_sha256":"559e304447c1ababe753e95e329293cf01068e2559fc67d841d048a7d1077e43","abstract_canon_sha256":"953e15f2f2746afce13cb699fddd133278fb37b287345a58b4a2617044348652"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:57:41.550822Z","signature_b64":"2YsHLUpEZirIchvegfGKZ7fijMENosDgAt2SJS9tgHamUbaJILMDtncNxtC+T5+q5GmUsTwRISC/lPupOpMhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f49e4d66183ad63050aacbe6c28cca5ba6b76b6a1119547ae167c8362e836b1f","last_reissued_at":"2026-05-18T01:57:41.550254Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:57:41.550254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Hong Shen, Zhigang Lu","submitted_at":"2015-05-29T01:20:42Z","abstract_excerpt":"The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, $k$NN attack discloses the target user's sensitive information by creating $k$ fake nearest neighbours by non-sensitive information. Among the current solutions against $k$NN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the gl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.00001","kind":"arxiv","version":2},"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":"1506.00001","created_at":"2026-05-18T01:57:41.550358+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.00001v2","created_at":"2026-05-18T01:57:41.550358+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.00001","created_at":"2026-05-18T01:57:41.550358+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SPE2ZQYHLLD","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SPE2ZQYHLLDAUFK","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SPE2ZQY","created_at":"2026-05-18T12:29:07.941421+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/6SPE2ZQYHLLDAUFKZPTMFDGKLO","json":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO.json","graph_json":"https://pith.science/api/pith-number/6SPE2ZQYHLLDAUFKZPTMFDGKLO/graph.json","events_json":"https://pith.science/api/pith-number/6SPE2ZQYHLLDAUFKZPTMFDGKLO/events.json","paper":"https://pith.science/paper/6SPE2ZQY"},"agent_actions":{"view_html":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO","download_json":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO.json","view_paper":"https://pith.science/paper/6SPE2ZQY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.00001&json=true","fetch_graph":"https://pith.science/api/pith-number/6SPE2ZQYHLLDAUFKZPTMFDGKLO/graph.json","fetch_events":"https://pith.science/api/pith-number/6SPE2ZQYHLLDAUFKZPTMFDGKLO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO/action/storage_attestation","attest_author":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO/action/author_attestation","sign_citation":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO/action/citation_signature","submit_replication":"https://pith.science/pith/6SPE2ZQYHLLDAUFKZPTMFDGKLO/action/replication_record"}},"created_at":"2026-05-18T01:57:41.550358+00:00","updated_at":"2026-05-18T01:57:41.550358+00:00"}