{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CBOF7EBJJ52CS5C4Z5MMY22W2K","short_pith_number":"pith:CBOF7EBJ","canonical_record":{"source":{"id":"2502.00245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T00:54:25Z","cross_cats_sorted":[],"title_canon_sha256":"a606dbbce3d4297c24cf46feced15243bd299c89d2d0d78ba610f3c2b930d0c4","abstract_canon_sha256":"69a26e589f31cb6a9aaf5850209ef0c8a3dfe8b6519be697ac806c58319f993f"},"schema_version":"1.0"},"canonical_sha256":"105c5f90294f7429745ccf58cc6b56d2a75cbb70a0a470788114d57e45a78885","source":{"kind":"arxiv","id":"2502.00245","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00245","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00245v1","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00245","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_12","alias_value":"CBOF7EBJJ52C","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_16","alias_value":"CBOF7EBJJ52CS5C4","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_8","alias_value":"CBOF7EBJ","created_at":"2026-07-05T10:08:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CBOF7EBJJ52CS5C4Z5MMY22W2K","target":"record","payload":{"canonical_record":{"source":{"id":"2502.00245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T00:54:25Z","cross_cats_sorted":[],"title_canon_sha256":"a606dbbce3d4297c24cf46feced15243bd299c89d2d0d78ba610f3c2b930d0c4","abstract_canon_sha256":"69a26e589f31cb6a9aaf5850209ef0c8a3dfe8b6519be697ac806c58319f993f"},"schema_version":"1.0"},"canonical_sha256":"105c5f90294f7429745ccf58cc6b56d2a75cbb70a0a470788114d57e45a78885","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:31.091060Z","signature_b64":"NjkHitU/jZ/5gswj7/vt6I8MMNc7yrZGMF4c2KvEwIdxgmr9gizMtHKhIi3ETjjRkFKrFZxPwPFcFQhyfCOxCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"105c5f90294f7429745ccf58cc6b56d2a75cbb70a0a470788114d57e45a78885","last_reissued_at":"2026-07-05T10:08:31.090632Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:31.090632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.00245","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":"2026-07-05T10:08:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+L9YmgKK+dKrFmtVC2xdnWGzy/UchiBtwedHBVSALP7slV2u/86/kZVfolHaN+Bu+Vr7T/CI6FHzxdKUMyhFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T11:48:47.123787Z"},"content_sha256":"6287e28a3fdb15a441ee570725ca8dce2e6c85ea044c21a267f3305626a57931","schema_version":"1.0","event_id":"sha256:6287e28a3fdb15a441ee570725ca8dce2e6c85ea044c21a267f3305626a57931"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CBOF7EBJJ52CS5C4Z5MMY22W2K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jianqing Zhang, Jingjing Liu, Peng Li, Tianyuan Zou, Xiaozhou Ye, Yang Liu, Ya-Qin Zhang, Ye Ouyang, Yufei Xiong","submitted_at":"2025-02-01T00:54:25Z","abstract_excerpt":"Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis %that avoid fine-tuning large pre-trained generative models often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00245","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/2502.00245/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":"2026-07-05T10:08:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DT4jZr9mLLGVqQ0pJ7rmvk6QUqGYAQMWb7ByjU6D1kwWIWm9cCn1bnaATHIO3I/qaD49k2+r2m829F4SWPgxCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-15T11:48:47.124173Z"},"content_sha256":"6e9aac94d0ef92b18605667e89ad9788d96f5f67009e5ff3d9028913f8cac0b9","schema_version":"1.0","event_id":"sha256:6e9aac94d0ef92b18605667e89ad9788d96f5f67009e5ff3d9028913f8cac0b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/bundle.json","state_url":"https://pith.science/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/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":"2026-07-15T11:48:47Z","links":{"resolver":"https://pith.science/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K","bundle":"https://pith.science/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/bundle.json","state":"https://pith.science/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CBOF7EBJJ52CS5C4Z5MMY22W2K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CBOF7EBJJ52CS5C4Z5MMY22W2K","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":"69a26e589f31cb6a9aaf5850209ef0c8a3dfe8b6519be697ac806c58319f993f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T00:54:25Z","title_canon_sha256":"a606dbbce3d4297c24cf46feced15243bd299c89d2d0d78ba610f3c2b930d0c4"},"schema_version":"1.0","source":{"id":"2502.00245","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00245","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00245v1","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00245","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_12","alias_value":"CBOF7EBJJ52C","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_16","alias_value":"CBOF7EBJJ52CS5C4","created_at":"2026-07-05T10:08:31Z"},{"alias_kind":"pith_short_8","alias_value":"CBOF7EBJ","created_at":"2026-07-05T10:08:31Z"}],"graph_snapshots":[{"event_id":"sha256:6e9aac94d0ef92b18605667e89ad9788d96f5f67009e5ff3d9028913f8cac0b9","target":"graph","created_at":"2026-07-05T10:08:31Z","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/2502.00245/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis %that avoid fine-tuning large pre-trained generative models often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias.","authors_text":"Jianqing Zhang, Jingjing Liu, Peng Li, Tianyuan Zou, Xiaozhou Ye, Yang Liu, Ya-Qin Zhang, Ye Ouyang, Yufei Xiong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T00:54:25Z","title":"Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00245","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":"sha256:6287e28a3fdb15a441ee570725ca8dce2e6c85ea044c21a267f3305626a57931","target":"record","created_at":"2026-07-05T10:08:31Z","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":"69a26e589f31cb6a9aaf5850209ef0c8a3dfe8b6519be697ac806c58319f993f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T00:54:25Z","title_canon_sha256":"a606dbbce3d4297c24cf46feced15243bd299c89d2d0d78ba610f3c2b930d0c4"},"schema_version":"1.0","source":{"id":"2502.00245","kind":"arxiv","version":1}},"canonical_sha256":"105c5f90294f7429745ccf58cc6b56d2a75cbb70a0a470788114d57e45a78885","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"105c5f90294f7429745ccf58cc6b56d2a75cbb70a0a470788114d57e45a78885","first_computed_at":"2026-07-05T10:08:31.090632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:08:31.090632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NjkHitU/jZ/5gswj7/vt6I8MMNc7yrZGMF4c2KvEwIdxgmr9gizMtHKhIi3ETjjRkFKrFZxPwPFcFQhyfCOxCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:08:31.091060Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.00245","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6287e28a3fdb15a441ee570725ca8dce2e6c85ea044c21a267f3305626a57931","sha256:6e9aac94d0ef92b18605667e89ad9788d96f5f67009e5ff3d9028913f8cac0b9"],"state_sha256":"f4f62f8dfa551da4ee0227c08b13e25f26f6253c7788c49060b75e80c767fe11"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A7MqUsY+I4iCN/22aQtyHsLz7fcgrihiI8czDw9GPP4kcZNgvfvM4scIXuhLNxQIFGM6XVhZZ5cRvEvukUyRDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-15T11:48:47.126577Z","bundle_sha256":"8362ee7160850da44d4f2669d271593734b6a2e81980593c54e39329c8c7dbb8"}}