{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KF7UC5HE62P6P6TIWSUPRKNPL4","short_pith_number":"pith:KF7UC5HE","canonical_record":{"source":{"id":"2202.02575","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-05T15:16:40Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1ceb81e29e49139c952855e2b9ab8c2b90344161c5623cad51604d8f74990921","abstract_canon_sha256":"859c35a4a256cfbe6b9b389943e116079893060832185da184340e4523ed220a"},"schema_version":"1.0"},"canonical_sha256":"517f4174e4f69fe7fa68b4a8f8a9af5f38b90fa540d3658daa3562ec39b19578","source":{"kind":"arxiv","id":"2202.02575","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02575","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02575v2","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02575","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_12","alias_value":"KF7UC5HE62P6","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_16","alias_value":"KF7UC5HE62P6P6TI","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_8","alias_value":"KF7UC5HE","created_at":"2026-07-05T06:30:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KF7UC5HE62P6P6TIWSUPRKNPL4","target":"record","payload":{"canonical_record":{"source":{"id":"2202.02575","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-05T15:16:40Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1ceb81e29e49139c952855e2b9ab8c2b90344161c5623cad51604d8f74990921","abstract_canon_sha256":"859c35a4a256cfbe6b9b389943e116079893060832185da184340e4523ed220a"},"schema_version":"1.0"},"canonical_sha256":"517f4174e4f69fe7fa68b4a8f8a9af5f38b90fa540d3658daa3562ec39b19578","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:24.282851Z","signature_b64":"gaQCMQaz4RcQuwj31eHxX1UjGur0kJKdhsFkzlmBGd+L56qOHEFLvyP0L2ayYCh4l9cCs6TM/q5xf4NtAcI6Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"517f4174e4f69fe7fa68b4a8f8a9af5f38b90fa540d3658daa3562ec39b19578","last_reissued_at":"2026-07-05T06:30:24.282296Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:24.282296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.02575","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":"2026-07-05T06:30:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NyiZx3LnGk0w6hJeWirQeL5Q/2o+a/82+7w3BiN/vrFWI4FRupxFMzHHA6zHV1wL+ZI8QPPxcHX6oqm6lfXbCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T14:54:05.866046Z"},"content_sha256":"5b4d55a0b43469e34501376b3ab4943f1e54f8ae64750a8f6a7498ea118e7b54","schema_version":"1.0","event_id":"sha256:5b4d55a0b43469e34501376b3ab4943f1e54f8ae64750a8f6a7498ea118e7b54"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KF7UC5HE62P6P6TIWSUPRKNPL4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Differentially Private Graph Classification with GNNs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Chinmay Prabhakar, Daniel Rueckert, Dmitrii Usynin, Georgios Kaissis, Johannes C. Paetzold, Tamara T. Mueller","submitted_at":"2022-02-05T15:16:40Z","abstract_excerpt":"Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein interactions and molecules. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02575","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/2202.02575/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":"2026-07-05T06:30:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"agLxBSe/sj7lHZlyF9O1GG6ov550pHEq7al45u+1LGRE7C4h83oYu7lf7+djPOrSAn6g++gf7nnyoB9WbnIhDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T14:54:05.866332Z"},"content_sha256":"764cd7fbe9f388d3b2bd75216fd2c3d82206f15740731e96802c81592cfe6b02","schema_version":"1.0","event_id":"sha256:764cd7fbe9f388d3b2bd75216fd2c3d82206f15740731e96802c81592cfe6b02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/bundle.json","state_url":"https://pith.science/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/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":"2026-08-01T14:54:05Z","links":{"resolver":"https://pith.science/pith/KF7UC5HE62P6P6TIWSUPRKNPL4","bundle":"https://pith.science/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/bundle.json","state":"https://pith.science/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KF7UC5HE62P6P6TIWSUPRKNPL4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KF7UC5HE62P6P6TIWSUPRKNPL4","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":"859c35a4a256cfbe6b9b389943e116079893060832185da184340e4523ed220a","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-05T15:16:40Z","title_canon_sha256":"1ceb81e29e49139c952855e2b9ab8c2b90344161c5623cad51604d8f74990921"},"schema_version":"1.0","source":{"id":"2202.02575","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02575","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02575v2","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02575","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_12","alias_value":"KF7UC5HE62P6","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_16","alias_value":"KF7UC5HE62P6P6TI","created_at":"2026-07-05T06:30:24Z"},{"alias_kind":"pith_short_8","alias_value":"KF7UC5HE","created_at":"2026-07-05T06:30:24Z"}],"graph_snapshots":[{"event_id":"sha256:764cd7fbe9f388d3b2bd75216fd2c3d82206f15740731e96802c81592cfe6b02","target":"graph","created_at":"2026-07-05T06:30:24Z","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/2202.02575/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein interactions and molecules. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of g","authors_text":"Chinmay Prabhakar, Daniel Rueckert, Dmitrii Usynin, Georgios Kaissis, Johannes C. Paetzold, Tamara T. Mueller","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-05T15:16:40Z","title":"Differentially Private Graph Classification with GNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02575","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":"sha256:5b4d55a0b43469e34501376b3ab4943f1e54f8ae64750a8f6a7498ea118e7b54","target":"record","created_at":"2026-07-05T06:30:24Z","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":"859c35a4a256cfbe6b9b389943e116079893060832185da184340e4523ed220a","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-05T15:16:40Z","title_canon_sha256":"1ceb81e29e49139c952855e2b9ab8c2b90344161c5623cad51604d8f74990921"},"schema_version":"1.0","source":{"id":"2202.02575","kind":"arxiv","version":2}},"canonical_sha256":"517f4174e4f69fe7fa68b4a8f8a9af5f38b90fa540d3658daa3562ec39b19578","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"517f4174e4f69fe7fa68b4a8f8a9af5f38b90fa540d3658daa3562ec39b19578","first_computed_at":"2026-07-05T06:30:24.282296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:30:24.282296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gaQCMQaz4RcQuwj31eHxX1UjGur0kJKdhsFkzlmBGd+L56qOHEFLvyP0L2ayYCh4l9cCs6TM/q5xf4NtAcI6Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:30:24.282851Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.02575","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b4d55a0b43469e34501376b3ab4943f1e54f8ae64750a8f6a7498ea118e7b54","sha256:764cd7fbe9f388d3b2bd75216fd2c3d82206f15740731e96802c81592cfe6b02"],"state_sha256":"eb9b6fa7c1f4ff81ec676f03e18fbfd839593212604c9d9282a90aa0999fe0e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9oQfekX/s/UZQsYuJ4ZiWiBuMGx10dnYiiHKEWLc+3E+ge93xH9kjYzJudGo3rWia7gT9iWCaiRC8epDAfmYAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T14:54:05.869763Z","bundle_sha256":"5c2da78625126e58daf14926483b3f6d8607f0b11436fbbc503291346c279696"}}