{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:C7VHLSWEIKJ62FD2VEYYGJXD5R","short_pith_number":"pith:C7VHLSWE","canonical_record":{"source":{"id":"2606.29748","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:44:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c278c4741bf7ff9581d2dbb25b7ef2cd6b6897cd0a4b3a2d3be4a24c1b3c5ff1","abstract_canon_sha256":"3ea58d8c6e3c9001c009107efc82815744dc51445dcae954db3a8b9dbc3ea55a"},"schema_version":"1.0"},"canonical_sha256":"17ea75cac44293ed147aa9318326e3ec558e9ec9a8bfd8f1d25f30b98069b595","source":{"kind":"arxiv","id":"2606.29748","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29748","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29748v1","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29748","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_12","alias_value":"C7VHLSWEIKJ6","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_16","alias_value":"C7VHLSWEIKJ62FD2","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_8","alias_value":"C7VHLSWE","created_at":"2026-06-30T02:17:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:C7VHLSWEIKJ62FD2VEYYGJXD5R","target":"record","payload":{"canonical_record":{"source":{"id":"2606.29748","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:44:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c278c4741bf7ff9581d2dbb25b7ef2cd6b6897cd0a4b3a2d3be4a24c1b3c5ff1","abstract_canon_sha256":"3ea58d8c6e3c9001c009107efc82815744dc51445dcae954db3a8b9dbc3ea55a"},"schema_version":"1.0"},"canonical_sha256":"17ea75cac44293ed147aa9318326e3ec558e9ec9a8bfd8f1d25f30b98069b595","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:33.362924Z","signature_b64":"6x/XPuLlEopbN7hu2fIZpvb61VyaLSn5LIboTlc8JZCX/jKG1SuINroR4hXXYVf2NnE+95lZ6TI9xtiOp82MBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17ea75cac44293ed147aa9318326e3ec558e9ec9a8bfd8f1d25f30b98069b595","last_reissued_at":"2026-06-30T02:17:33.362281Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:33.362281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.29748","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T02:17:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Ro45I2na13m9e2CuyU1CnkKx8xHkm8iVafhBGivJ4T4cXQly3Xsz7Ej0UG/+FIwI7Lx8UbVoIn0oXaUxv3ZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T13:43:47.573822Z"},"content_sha256":"7abc08b2ae0deb15f4d67787cba83f51b86de3fd78a13fae4a0d7f74aa37f27a","schema_version":"1.0","event_id":"sha256:7abc08b2ae0deb15f4d67787cba83f51b86de3fd78a13fae4a0d7f74aa37f27a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:C7VHLSWEIKJ62FD2VEYYGJXD5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Generative Reconstruction Attacks against Graph Neural Network Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Adebayo Keji, Sayanton Dibbo","submitted_at":"2026-06-29T03:44:51Z","abstract_excerpt":"The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive. The non-Euclidean nature of the graph data makes the analysis computationally challenging, leading to the use of Graph Neural Networks (GNNs) in the age of AI. GNNs may inadvertently leak sensitive data they are trained on, which raises serious data security issues, including the model inversion attack. In this study, we analyze GNNs' vulnerabilities by introducing two novel graph inversion (i.e., reconstruction) attacks: graph-label co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29748","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/2606.29748/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T02:17:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"28VTZoEuCsSiHSFbOAFz4AlJgk6JontVUGGvkyFkrL5jRl2eDc4HqgOLOcn7HE+mjD5MERc0I91oEpO81YHKCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T13:43:47.574207Z"},"content_sha256":"a37d3105d8656152379d176575bd6fdb82702fe04ac8ef9e0daec8ec4107f0d7","schema_version":"1.0","event_id":"sha256:a37d3105d8656152379d176575bd6fdb82702fe04ac8ef9e0daec8ec4107f0d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/bundle.json","state_url":"https://pith.science/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-01T13:43:47Z","links":{"resolver":"https://pith.science/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R","bundle":"https://pith.science/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/bundle.json","state":"https://pith.science/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C7VHLSWEIKJ62FD2VEYYGJXD5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:C7VHLSWEIKJ62FD2VEYYGJXD5R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3ea58d8c6e3c9001c009107efc82815744dc51445dcae954db3a8b9dbc3ea55a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:44:51Z","title_canon_sha256":"c278c4741bf7ff9581d2dbb25b7ef2cd6b6897cd0a4b3a2d3be4a24c1b3c5ff1"},"schema_version":"1.0","source":{"id":"2606.29748","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29748","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29748v1","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29748","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_12","alias_value":"C7VHLSWEIKJ6","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_16","alias_value":"C7VHLSWEIKJ62FD2","created_at":"2026-06-30T02:17:33Z"},{"alias_kind":"pith_short_8","alias_value":"C7VHLSWE","created_at":"2026-06-30T02:17:33Z"}],"graph_snapshots":[{"event_id":"sha256:a37d3105d8656152379d176575bd6fdb82702fe04ac8ef9e0daec8ec4107f0d7","target":"graph","created_at":"2026-06-30T02:17:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.29748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive. The non-Euclidean nature of the graph data makes the analysis computationally challenging, leading to the use of Graph Neural Networks (GNNs) in the age of AI. GNNs may inadvertently leak sensitive data they are trained on, which raises serious data security issues, including the model inversion attack. In this study, we analyze GNNs' vulnerabilities by introducing two novel graph inversion (i.e., reconstruction) attacks: graph-label co","authors_text":"Adebayo Keji, Sayanton Dibbo","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:44:51Z","title":"Rethinking Generative Reconstruction Attacks against Graph Neural Network Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29748","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7abc08b2ae0deb15f4d67787cba83f51b86de3fd78a13fae4a0d7f74aa37f27a","target":"record","created_at":"2026-06-30T02:17:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3ea58d8c6e3c9001c009107efc82815744dc51445dcae954db3a8b9dbc3ea55a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:44:51Z","title_canon_sha256":"c278c4741bf7ff9581d2dbb25b7ef2cd6b6897cd0a4b3a2d3be4a24c1b3c5ff1"},"schema_version":"1.0","source":{"id":"2606.29748","kind":"arxiv","version":1}},"canonical_sha256":"17ea75cac44293ed147aa9318326e3ec558e9ec9a8bfd8f1d25f30b98069b595","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17ea75cac44293ed147aa9318326e3ec558e9ec9a8bfd8f1d25f30b98069b595","first_computed_at":"2026-06-30T02:17:33.362281Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T02:17:33.362281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6x/XPuLlEopbN7hu2fIZpvb61VyaLSn5LIboTlc8JZCX/jKG1SuINroR4hXXYVf2NnE+95lZ6TI9xtiOp82MBw==","signature_status":"signed_v1","signed_at":"2026-06-30T02:17:33.362924Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29748","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7abc08b2ae0deb15f4d67787cba83f51b86de3fd78a13fae4a0d7f74aa37f27a","sha256:a37d3105d8656152379d176575bd6fdb82702fe04ac8ef9e0daec8ec4107f0d7"],"state_sha256":"41cbabd0629b0c0f65f2c53ec1b596fcb0890721d91751d0dd5e9cd5ecaf6e97"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5020DmRaPBjGNlDY+ta0LquVlU5TGehEuF+H5nqg9ZAYsP9fBIdLThqUKFgECbYcoweIF33IgH1Bz7CYUi3ECQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T13:43:47.577065Z","bundle_sha256":"6232c5f688fe0558cc3802c8da4631b384a46d7feef2ebf2f0c5c01c22d84876"}}