{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2DI6BD2YIZGHGOOZR4BZLBB34C","short_pith_number":"pith:2DI6BD2Y","canonical_record":{"source":{"id":"2505.20026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T14:17:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"48852dce16cf591c1a56ef96984d7415fca14ebd6ff4eeff281fc5a8bfae5652","abstract_canon_sha256":"cc853d14950a63efcecf48d8bb831bf015751b85a2b45eef94994c761c24a4a6"},"schema_version":"1.0"},"canonical_sha256":"d0d1e08f58464c7339d98f0395843be09d3ad2ca61bc88ebdc900aa8c6098937","source":{"kind":"arxiv","id":"2505.20026","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20026","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20026v1","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20026","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_12","alias_value":"2DI6BD2YIZGH","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_16","alias_value":"2DI6BD2YIZGHGOOZ","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_8","alias_value":"2DI6BD2Y","created_at":"2026-07-05T11:09:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2DI6BD2YIZGHGOOZR4BZLBB34C","target":"record","payload":{"canonical_record":{"source":{"id":"2505.20026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T14:17:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"48852dce16cf591c1a56ef96984d7415fca14ebd6ff4eeff281fc5a8bfae5652","abstract_canon_sha256":"cc853d14950a63efcecf48d8bb831bf015751b85a2b45eef94994c761c24a4a6"},"schema_version":"1.0"},"canonical_sha256":"d0d1e08f58464c7339d98f0395843be09d3ad2ca61bc88ebdc900aa8c6098937","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:51.438281Z","signature_b64":"ZtueQnkc0l0YTNgvWbijCjOcuxZWXzEMndiV0DNrhVNY61+cAUm5NsP4ZGDFbdkpKmHdF9s6EtrwYA2ane40DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0d1e08f58464c7339d98f0395843be09d3ad2ca61bc88ebdc900aa8c6098937","last_reissued_at":"2026-07-05T11:09:51.437866Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:51.437866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.20026","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":"2026-07-05T11:09:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1y33oL6axF0nJEkYOTyVsiUD1KeRnnrNjC2uzfksJ9WB1YiZNNeEWsFeEWMd8+nmKfGUtsOTTgA8dbhemN6pAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:59:04.482437Z"},"content_sha256":"41a7a6e3a23f334b65d75af7a71c9d290470ce0dd6cd35bb0d2ea7464fbf9fa4","schema_version":"1.0","event_id":"sha256:41a7a6e3a23f334b65d75af7a71c9d290470ce0dd6cd35bb0d2ea7464fbf9fa4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2DI6BD2YIZGHGOOZR4BZLBB34C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chen Liu, Xinping Chen","submitted_at":"2025-05-26T14:17:00Z","abstract_excerpt":"We propose Gradient Inversion Transcript (GIT), a novel generative approach for reconstructing training data from leaked gradients. GIT employs a generative attack model, whose architecture is tailored to align with the structure of the leaked model based on theoretical analysis. Once trained offline, GIT can be deployed efficiently and only relies on the leaked gradients to reconstruct the input data, rendering it applicable under various distributed learning environments. When used as a prior for other iterative optimization-based methods, GIT not only accelerates convergence but also enhanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20026","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/2505.20026/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":"2026-07-05T11:09:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rPn6uZQTWGO1hKp1apTE7Br3YerusbZsuAlQbrimC5KPdHO3G8CIsTSQQXZetqDfOXJKSqXvekbn+YzAKfhuBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:59:04.483204Z"},"content_sha256":"a2a7a5681e69f651f70dbb4075ecb7cb9c2a1cecfe5e0e4c6a9adae816f89bf6","schema_version":"1.0","event_id":"sha256:a2a7a5681e69f651f70dbb4075ecb7cb9c2a1cecfe5e0e4c6a9adae816f89bf6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/bundle.json","state_url":"https://pith.science/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/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":"2026-08-09T10:59:04Z","links":{"resolver":"https://pith.science/pith/2DI6BD2YIZGHGOOZR4BZLBB34C","bundle":"https://pith.science/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/bundle.json","state":"https://pith.science/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2DI6BD2YIZGHGOOZR4BZLBB34C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2DI6BD2YIZGHGOOZR4BZLBB34C","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":"cc853d14950a63efcecf48d8bb831bf015751b85a2b45eef94994c761c24a4a6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T14:17:00Z","title_canon_sha256":"48852dce16cf591c1a56ef96984d7415fca14ebd6ff4eeff281fc5a8bfae5652"},"schema_version":"1.0","source":{"id":"2505.20026","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20026","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20026v1","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20026","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_12","alias_value":"2DI6BD2YIZGH","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_16","alias_value":"2DI6BD2YIZGHGOOZ","created_at":"2026-07-05T11:09:51Z"},{"alias_kind":"pith_short_8","alias_value":"2DI6BD2Y","created_at":"2026-07-05T11:09:51Z"}],"graph_snapshots":[{"event_id":"sha256:a2a7a5681e69f651f70dbb4075ecb7cb9c2a1cecfe5e0e4c6a9adae816f89bf6","target":"graph","created_at":"2026-07-05T11:09:51Z","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/2505.20026/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose Gradient Inversion Transcript (GIT), a novel generative approach for reconstructing training data from leaked gradients. GIT employs a generative attack model, whose architecture is tailored to align with the structure of the leaked model based on theoretical analysis. Once trained offline, GIT can be deployed efficiently and only relies on the leaked gradients to reconstruct the input data, rendering it applicable under various distributed learning environments. When used as a prior for other iterative optimization-based methods, GIT not only accelerates convergence but also enhanc","authors_text":"Chen Liu, Xinping Chen","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T14:17:00Z","title":"Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20026","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":"sha256:41a7a6e3a23f334b65d75af7a71c9d290470ce0dd6cd35bb0d2ea7464fbf9fa4","target":"record","created_at":"2026-07-05T11:09:51Z","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":"cc853d14950a63efcecf48d8bb831bf015751b85a2b45eef94994c761c24a4a6","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T14:17:00Z","title_canon_sha256":"48852dce16cf591c1a56ef96984d7415fca14ebd6ff4eeff281fc5a8bfae5652"},"schema_version":"1.0","source":{"id":"2505.20026","kind":"arxiv","version":1}},"canonical_sha256":"d0d1e08f58464c7339d98f0395843be09d3ad2ca61bc88ebdc900aa8c6098937","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0d1e08f58464c7339d98f0395843be09d3ad2ca61bc88ebdc900aa8c6098937","first_computed_at":"2026-07-05T11:09:51.437866Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:51.437866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZtueQnkc0l0YTNgvWbijCjOcuxZWXzEMndiV0DNrhVNY61+cAUm5NsP4ZGDFbdkpKmHdF9s6EtrwYA2ane40DA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:51.438281Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.20026","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41a7a6e3a23f334b65d75af7a71c9d290470ce0dd6cd35bb0d2ea7464fbf9fa4","sha256:a2a7a5681e69f651f70dbb4075ecb7cb9c2a1cecfe5e0e4c6a9adae816f89bf6"],"state_sha256":"07e3785bfb817fecac10c53dd96480674be82b59cce288a38d30358e462ec8a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R/BpdatnlrIgpTSptJfeCEI8p5p4ALnjh+PTrWN0E1ELiPAUmhMbMvxrbI131ZjWdregNCiBm+HEM6C0yowMCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T10:59:04.488142Z","bundle_sha256":"367fc38354629706aca01ad502318c804308969de0058fe9e28a214b5d1b6842"}}