{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IJM7YBASNZQ3O2EC2WNKA5XH6Y","short_pith_number":"pith:IJM7YBAS","schema_version":"1.0","canonical_sha256":"4259fc04126e61b76882d59aa076e7f61e6e3a56fee1590adb953ca8b59ed7f1","source":{"kind":"arxiv","id":"1911.09872","version":1},"attestation_state":"computed","paper":{"title":"Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.IR"],"primary_cat":"cs.SI","authors_text":"Ahmadreza Mosallanezhad, Alexander Nou, Ghazaleh Beigi, Hamidreza Alvari, Huan Liu, Ruocheng Guo","submitted_at":"2019-11-22T06:22:21Z","abstract_excerpt":"Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which sim"},"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":"1911.09872","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-11-22T06:22:21Z","cross_cats_sorted":["cs.CR","cs.IR"],"title_canon_sha256":"8c072a25868d126648219704b0991fe5ce50c3c07f37d833333371a6aa801b87","abstract_canon_sha256":"d137fb014e4f52e756e475b36251ef4310a269d8f749fc3c7c09bb83435a0fd5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:21:18.509348Z","signature_b64":"GSd5+WGsM6/x77AqMTMPTBDJE26r6zJFVTxinx3JJWwnLTVpCpmGdHD6gPY2cv0BKAn3m3bocAtbNMrLpRhPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4259fc04126e61b76882d59aa076e7f61e6e3a56fee1590adb953ca8b59ed7f1","last_reissued_at":"2026-07-05T00:21:18.508766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:21:18.508766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.IR"],"primary_cat":"cs.SI","authors_text":"Ahmadreza Mosallanezhad, Alexander Nou, Ghazaleh Beigi, Hamidreza Alvari, Huan Liu, Ruocheng Guo","submitted_at":"2019-11-22T06:22:21Z","abstract_excerpt":"Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which sim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09872","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/1911.09872/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":"1911.09872","created_at":"2026-07-05T00:21:18.508834+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.09872v1","created_at":"2026-07-05T00:21:18.508834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09872","created_at":"2026-07-05T00:21:18.508834+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJM7YBASNZQ3","created_at":"2026-07-05T00:21:18.508834+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJM7YBASNZQ3O2EC","created_at":"2026-07-05T00:21:18.508834+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJM7YBAS","created_at":"2026-07-05T00:21:18.508834+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/IJM7YBASNZQ3O2EC2WNKA5XH6Y","json":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y.json","graph_json":"https://pith.science/api/pith-number/IJM7YBASNZQ3O2EC2WNKA5XH6Y/graph.json","events_json":"https://pith.science/api/pith-number/IJM7YBASNZQ3O2EC2WNKA5XH6Y/events.json","paper":"https://pith.science/paper/IJM7YBAS"},"agent_actions":{"view_html":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y","download_json":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y.json","view_paper":"https://pith.science/paper/IJM7YBAS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.09872&json=true","fetch_graph":"https://pith.science/api/pith-number/IJM7YBASNZQ3O2EC2WNKA5XH6Y/graph.json","fetch_events":"https://pith.science/api/pith-number/IJM7YBASNZQ3O2EC2WNKA5XH6Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y/action/storage_attestation","attest_author":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y/action/author_attestation","sign_citation":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y/action/citation_signature","submit_replication":"https://pith.science/pith/IJM7YBASNZQ3O2EC2WNKA5XH6Y/action/replication_record"}},"created_at":"2026-07-05T00:21:18.508834+00:00","updated_at":"2026-07-05T00:21:18.508834+00:00"}