{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QXSAL5UXZNY44QOSXRQV65FMYN","short_pith_number":"pith:QXSAL5UX","schema_version":"1.0","canonical_sha256":"85e405f697cb71ce41d2bc615f74acc35796eec6524cefafa6e95dde93824b51","source":{"kind":"arxiv","id":"2507.19964","version":1},"attestation_state":"computed","paper":{"title":"Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Di Wu, Jing Xu, Jun Bai, Kunhao Li, Lei Yang, Taotao Cai, Wencheng Yang, Yan Li, Yiliao Song, Ziyi Zhang","submitted_at":"2025-07-26T14:32:38Z","abstract_excerpt":"Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from such data due to their strong representation capabilities. As GNNs are increasingly deployed in federated learning (FL) settings to preserve data locality and privacy, new privacy threats arise from the interaction between graph structures and decentralized training. In this paper, we present the first systematic study of cross-client membership inference attacks (CC-MIA) against"},"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":"2507.19964","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T14:32:38Z","cross_cats_sorted":[],"title_canon_sha256":"f2a1b90655f72bb6a8b15a18d5611721388edac77369c5746cf92b2fd0158065","abstract_canon_sha256":"d339d5545e9cf1ba6c2e178af75b9b6d39474746d310e70108aff1dbf0c167f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:45.719457Z","signature_b64":"TV1CA7dP7QyD4P65wUZ1ZfEkDsQbaHNsRl4x239AWg2m1h/wOeiWchI5Y1wPBW3wmLMM6uKHs5oFkHnluGRoCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85e405f697cb71ce41d2bc615f74acc35796eec6524cefafa6e95dde93824b51","last_reissued_at":"2026-07-05T11:43:45.718959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:45.718959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Di Wu, Jing Xu, Jun Bai, Kunhao Li, Lei Yang, Taotao Cai, Wencheng Yang, Yan Li, Yiliao Song, Ziyi Zhang","submitted_at":"2025-07-26T14:32:38Z","abstract_excerpt":"Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from such data due to their strong representation capabilities. As GNNs are increasingly deployed in federated learning (FL) settings to preserve data locality and privacy, new privacy threats arise from the interaction between graph structures and decentralized training. In this paper, we present the first systematic study of cross-client membership inference attacks (CC-MIA) against"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19964","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/2507.19964/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":"2507.19964","created_at":"2026-07-05T11:43:45.719023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.19964v1","created_at":"2026-07-05T11:43:45.719023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19964","created_at":"2026-07-05T11:43:45.719023+00:00"},{"alias_kind":"pith_short_12","alias_value":"QXSAL5UXZNY4","created_at":"2026-07-05T11:43:45.719023+00:00"},{"alias_kind":"pith_short_16","alias_value":"QXSAL5UXZNY44QOS","created_at":"2026-07-05T11:43:45.719023+00:00"},{"alias_kind":"pith_short_8","alias_value":"QXSAL5UX","created_at":"2026-07-05T11:43:45.719023+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/QXSAL5UXZNY44QOSXRQV65FMYN","json":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN.json","graph_json":"https://pith.science/api/pith-number/QXSAL5UXZNY44QOSXRQV65FMYN/graph.json","events_json":"https://pith.science/api/pith-number/QXSAL5UXZNY44QOSXRQV65FMYN/events.json","paper":"https://pith.science/paper/QXSAL5UX"},"agent_actions":{"view_html":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN","download_json":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN.json","view_paper":"https://pith.science/paper/QXSAL5UX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.19964&json=true","fetch_graph":"https://pith.science/api/pith-number/QXSAL5UXZNY44QOSXRQV65FMYN/graph.json","fetch_events":"https://pith.science/api/pith-number/QXSAL5UXZNY44QOSXRQV65FMYN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN/action/storage_attestation","attest_author":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN/action/author_attestation","sign_citation":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN/action/citation_signature","submit_replication":"https://pith.science/pith/QXSAL5UXZNY44QOSXRQV65FMYN/action/replication_record"}},"created_at":"2026-07-05T11:43:45.719023+00:00","updated_at":"2026-07-05T11:43:45.719023+00:00"}