{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:54BF2EY6GRYPKUIYPZ57DD6WYM","short_pith_number":"pith:54BF2EY6","canonical_record":{"source":{"id":"2309.08569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-15T17:35:51Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"2ae6c67a36eeecf59a999ba85ff274968f0f9d9644b43644eafd039e9095d7a1","abstract_canon_sha256":"268e56e54cddd4880b7d172c20d3ce8e9cb1bed2c274bfd020954707bceed195"},"schema_version":"1.0"},"canonical_sha256":"ef025d131e3470f551187e7bf18fd6c33886fe574734f1b52917f46a6e1c78f0","source":{"kind":"arxiv","id":"2309.08569","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.08569","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.08569v2","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.08569","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"54BF2EY6GRYP","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"54BF2EY6GRYPKUIY","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"54BF2EY6","created_at":"2026-07-05T08:52:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:54BF2EY6GRYPKUIYPZ57DD6WYM","target":"record","payload":{"canonical_record":{"source":{"id":"2309.08569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-15T17:35:51Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"2ae6c67a36eeecf59a999ba85ff274968f0f9d9644b43644eafd039e9095d7a1","abstract_canon_sha256":"268e56e54cddd4880b7d172c20d3ce8e9cb1bed2c274bfd020954707bceed195"},"schema_version":"1.0"},"canonical_sha256":"ef025d131e3470f551187e7bf18fd6c33886fe574734f1b52917f46a6e1c78f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:26.523724Z","signature_b64":"B1lxMKIHLBTxCpsF4DjevSJqLjt5AtpercI4dpp/oq7J5yRnrGr5x7v8l9QP3uLdmzQNzGZIPeZe5IkbAs/5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef025d131e3470f551187e7bf18fd6c33886fe574734f1b52917f46a6e1c78f0","last_reissued_at":"2026-07-05T08:52:26.523373Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:26.523373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.08569","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RSzfVSgsq6l/hsC3OEQS2B8pJVLm81M30VlcWo7c9ZeL3vDAxWxd6d26/yE4RwTztXtlKkfPGFF0QHrgVFgiAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:38:21.819042Z"},"content_sha256":"132823c4827cca88c3676df97a982e57ac2b3d68cc61682a3c28531974aff9a6","schema_version":"1.0","event_id":"sha256:132823c4827cca88c3676df97a982e57ac2b3d68cc61682a3c28531974aff9a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:54BF2EY6GRYPKUIYPZ57DD6WYM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Karuna Bhaila, Wen Huang, Xintao Wu, Yongkai Wu","submitted_at":"2023-09-15T17:35:51Z","abstract_excerpt":"Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide node privacy at the user level, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training. Specifically, we inves"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.08569","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.08569/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":"2026-07-05T08:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oHdzw+D/HnQ8ncwkP6EjuELZ/1zidD3BG5D0NDoi7FvqbyaiEpjiKil0OKNbbM/XLw9k6lZLOCrXnn0URLFgBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:38:21.820035Z"},"content_sha256":"d791a4e28a5c9e1067d06c65719b73076a6a3ce8908f8092b7da8b65b91e4d5e","schema_version":"1.0","event_id":"sha256:d791a4e28a5c9e1067d06c65719b73076a6a3ce8908f8092b7da8b65b91e4d5e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/bundle.json","state_url":"https://pith.science/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/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":"2026-08-08T09:38:21Z","links":{"resolver":"https://pith.science/pith/54BF2EY6GRYPKUIYPZ57DD6WYM","bundle":"https://pith.science/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/bundle.json","state":"https://pith.science/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/54BF2EY6GRYPKUIYPZ57DD6WYM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:54BF2EY6GRYPKUIYPZ57DD6WYM","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":"268e56e54cddd4880b7d172c20d3ce8e9cb1bed2c274bfd020954707bceed195","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-15T17:35:51Z","title_canon_sha256":"2ae6c67a36eeecf59a999ba85ff274968f0f9d9644b43644eafd039e9095d7a1"},"schema_version":"1.0","source":{"id":"2309.08569","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.08569","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.08569v2","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.08569","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"54BF2EY6GRYP","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"54BF2EY6GRYPKUIY","created_at":"2026-07-05T08:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"54BF2EY6","created_at":"2026-07-05T08:52:26Z"}],"graph_snapshots":[{"event_id":"sha256:d791a4e28a5c9e1067d06c65719b73076a6a3ce8908f8092b7da8b65b91e4d5e","target":"graph","created_at":"2026-07-05T08:52:26Z","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/2309.08569/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide node privacy at the user level, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training. Specifically, we inves","authors_text":"Karuna Bhaila, Wen Huang, Xintao Wu, Yongkai Wu","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-15T17:35:51Z","title":"Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.08569","kind":"arxiv","version":2},"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":"sha256:132823c4827cca88c3676df97a982e57ac2b3d68cc61682a3c28531974aff9a6","target":"record","created_at":"2026-07-05T08:52:26Z","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":"268e56e54cddd4880b7d172c20d3ce8e9cb1bed2c274bfd020954707bceed195","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-15T17:35:51Z","title_canon_sha256":"2ae6c67a36eeecf59a999ba85ff274968f0f9d9644b43644eafd039e9095d7a1"},"schema_version":"1.0","source":{"id":"2309.08569","kind":"arxiv","version":2}},"canonical_sha256":"ef025d131e3470f551187e7bf18fd6c33886fe574734f1b52917f46a6e1c78f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef025d131e3470f551187e7bf18fd6c33886fe574734f1b52917f46a6e1c78f0","first_computed_at":"2026-07-05T08:52:26.523373Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:26.523373Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B1lxMKIHLBTxCpsF4DjevSJqLjt5AtpercI4dpp/oq7J5yRnrGr5x7v8l9QP3uLdmzQNzGZIPeZe5IkbAs/5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:26.523724Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.08569","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:132823c4827cca88c3676df97a982e57ac2b3d68cc61682a3c28531974aff9a6","sha256:d791a4e28a5c9e1067d06c65719b73076a6a3ce8908f8092b7da8b65b91e4d5e"],"state_sha256":"005a75f50b6fd41afefe04da276ce1126560b6eb85b7cdc74aa145e137be06f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vw71vdjFTCDFqQOqyYDsss31AE3JW++w9KMA2JVygt5iXOniMw203LWR6tppiEM+oem784HBEg1hiVSgM1ydCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:38:21.825298Z","bundle_sha256":"f84746dfa101c4ed035c548c70edd3d7ad95bed398c2377e0bcf01bad6f5eb58"}}