{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E5D2MQVK5BGYTTTH3BXH3S5YV7","short_pith_number":"pith:E5D2MQVK","schema_version":"1.0","canonical_sha256":"2747a642aae84d89ce67d86e7dcbb8affafdfec8f03a0bc0ca4e3769ba26c2f4","source":{"kind":"arxiv","id":"2406.01394","version":5},"attestation_state":"computed","paper":{"title":"PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Cen Chen, Haoran Li, Huiping Zhuang, Jianwei Wang, Junyao Yang, Zhengdong Lu, Ziqian Zeng","submitted_at":"2024-06-03T14:57:39Z","abstract_excerpt":"The widespread usage of online Large Language Models (LLMs) inference services has raised significant privacy concerns about the potential exposure of private information in user inputs to malicious eavesdroppers. Existing privacy protection methods for LLMs suffer from either insufficient privacy protection, performance degradation, or large inference time overhead. To address these limitations, we propose PrivacyRestore, a plug-and-play method to protect the privacy of user inputs during LLM inference. The server first trains restoration vectors for each privacy span and then release to clie"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.01394","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-06-03T14:57:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dac5c7965fbd4671dbab841f385db19bfc9adc485e06478e11c6ce05f4f84289","abstract_canon_sha256":"df13023e9d8095b37384a8ae9cf8b269dfb3d104a26a76b18151a49e5e848e2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:46.003161Z","signature_b64":"k7yiQ8awPhjoSlziqROcYjMK70q+AQExv4LBj4pxOVcwGKn0TCD/bPNvB3nux278GYLC7JNq7BJ+6K9zSDZOAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2747a642aae84d89ce67d86e7dcbb8affafdfec8f03a0bc0ca4e3769ba26c2f4","last_reissued_at":"2026-07-05T11:10:46.002658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:46.002658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Cen Chen, Haoran Li, Huiping Zhuang, Jianwei Wang, Junyao Yang, Zhengdong Lu, Ziqian Zeng","submitted_at":"2024-06-03T14:57:39Z","abstract_excerpt":"The widespread usage of online Large Language Models (LLMs) inference services has raised significant privacy concerns about the potential exposure of private information in user inputs to malicious eavesdroppers. Existing privacy protection methods for LLMs suffer from either insufficient privacy protection, performance degradation, or large inference time overhead. To address these limitations, we propose PrivacyRestore, a plug-and-play method to protect the privacy of user inputs during LLM inference. The server first trains restoration vectors for each privacy span and then release to clie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01394","kind":"arxiv","version":5},"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/2406.01394/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.01394","created_at":"2026-07-05T11:10:46.002718+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01394v5","created_at":"2026-07-05T11:10:46.002718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01394","created_at":"2026-07-05T11:10:46.002718+00:00"},{"alias_kind":"pith_short_12","alias_value":"E5D2MQVK5BGY","created_at":"2026-07-05T11:10:46.002718+00:00"},{"alias_kind":"pith_short_16","alias_value":"E5D2MQVK5BGYTTTH","created_at":"2026-07-05T11:10:46.002718+00:00"},{"alias_kind":"pith_short_8","alias_value":"E5D2MQVK","created_at":"2026-07-05T11:10:46.002718+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09132","citing_title":"Vision Language Model Helps Private Information De-Identification in Vision Data","ref_index":82,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7","json":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7.json","graph_json":"https://pith.science/api/pith-number/E5D2MQVK5BGYTTTH3BXH3S5YV7/graph.json","events_json":"https://pith.science/api/pith-number/E5D2MQVK5BGYTTTH3BXH3S5YV7/events.json","paper":"https://pith.science/paper/E5D2MQVK"},"agent_actions":{"view_html":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7","download_json":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7.json","view_paper":"https://pith.science/paper/E5D2MQVK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01394&json=true","fetch_graph":"https://pith.science/api/pith-number/E5D2MQVK5BGYTTTH3BXH3S5YV7/graph.json","fetch_events":"https://pith.science/api/pith-number/E5D2MQVK5BGYTTTH3BXH3S5YV7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7/action/storage_attestation","attest_author":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7/action/author_attestation","sign_citation":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7/action/citation_signature","submit_replication":"https://pith.science/pith/E5D2MQVK5BGYTTTH3BXH3S5YV7/action/replication_record"}},"created_at":"2026-07-05T11:10:46.002718+00:00","updated_at":"2026-07-05T11:10:46.002718+00:00"}