{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SAHVPI4IBNJOPZRSFMUHXEXO3M","short_pith_number":"pith:SAHVPI4I","canonical_record":{"source":{"id":"2412.06113","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T00:24:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"413da0388c3aebf933169cfe983ceb8d82916cfe3f4b70d658fe01964bcf3642","abstract_canon_sha256":"b133463786e7118dd1525fce1ea3d51b0e5c76df5a16e074d85e202fcb5be6ea"},"schema_version":"1.0"},"canonical_sha256":"900f57a3880b52e7e6322b287b92eedb0848f71eb34cc647b4911c8c9eaa8f74","source":{"kind":"arxiv","id":"2412.06113","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.06113","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.06113v1","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06113","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_12","alias_value":"SAHVPI4IBNJO","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_16","alias_value":"SAHVPI4IBNJOPZRS","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_8","alias_value":"SAHVPI4I","created_at":"2026-07-05T09:46:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SAHVPI4IBNJOPZRSFMUHXEXO3M","target":"record","payload":{"canonical_record":{"source":{"id":"2412.06113","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T00:24:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"413da0388c3aebf933169cfe983ceb8d82916cfe3f4b70d658fe01964bcf3642","abstract_canon_sha256":"b133463786e7118dd1525fce1ea3d51b0e5c76df5a16e074d85e202fcb5be6ea"},"schema_version":"1.0"},"canonical_sha256":"900f57a3880b52e7e6322b287b92eedb0848f71eb34cc647b4911c8c9eaa8f74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:15.239799Z","signature_b64":"BtjlpUbghGnhgEmaFaYL6D6VJfoKvkOz2tPLXR/RSe5tbxqOzA9eGP0R0NZj8RS+ms6nZa7gOo5XcwbnKFj4Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"900f57a3880b52e7e6322b287b92eedb0848f71eb34cc647b4911c8c9eaa8f74","last_reissued_at":"2026-07-05T09:46:15.239340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:15.239340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.06113","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":"2026-07-05T09:46:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kAZYIbsKOZrtLfUXYmgKMLKwMdaFiblarGj/VIK38MfAI1bsGtT7Zo7t1eifsqe429HVEBoRbuW8iJsGPbhBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:48:37.555160Z"},"content_sha256":"481a9a30a5a134e5d58bd4564c74c0a5729de8fd1706b82963f73cd387b64445","schema_version":"1.0","event_id":"sha256:481a9a30a5a134e5d58bd4564c74c0a5729de8fd1706b82963f73cd387b64445"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SAHVPI4IBNJOPZRSFMUHXEXO3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Eric Song, Guoshenghui Zhao","submitted_at":"2024-12-09T00:24:09Z","abstract_excerpt":"The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This survey comprehensively explores the landscape of privacy-preserving mechanisms tailored for LLMs, including differential privacy, federated learning, cryptographic protocols, and trusted execution environments. We examine their efficacy in add"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06113","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/2412.06113/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":"2026-07-05T09:46:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V47ijUwWUTr9kvhI7Njzp4eegHc+tsQrp4E4j1uuqM3ixcKxuUzpb5XAvJMUSDUyuJp0CaXQOWs9FQEggLA5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:48:37.555668Z"},"content_sha256":"726f7b8382351a0b38b4eabdd0b07a62d7acf70a1b6aedcb2e37b2929fdbf1db","schema_version":"1.0","event_id":"sha256:726f7b8382351a0b38b4eabdd0b07a62d7acf70a1b6aedcb2e37b2929fdbf1db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/bundle.json","state_url":"https://pith.science/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/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":"2026-08-09T08:48:37Z","links":{"resolver":"https://pith.science/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M","bundle":"https://pith.science/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/bundle.json","state":"https://pith.science/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SAHVPI4IBNJOPZRSFMUHXEXO3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SAHVPI4IBNJOPZRSFMUHXEXO3M","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":"b133463786e7118dd1525fce1ea3d51b0e5c76df5a16e074d85e202fcb5be6ea","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T00:24:09Z","title_canon_sha256":"413da0388c3aebf933169cfe983ceb8d82916cfe3f4b70d658fe01964bcf3642"},"schema_version":"1.0","source":{"id":"2412.06113","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.06113","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"arxiv_version","alias_value":"2412.06113v1","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06113","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_12","alias_value":"SAHVPI4IBNJO","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_16","alias_value":"SAHVPI4IBNJOPZRS","created_at":"2026-07-05T09:46:15Z"},{"alias_kind":"pith_short_8","alias_value":"SAHVPI4I","created_at":"2026-07-05T09:46:15Z"}],"graph_snapshots":[{"event_id":"sha256:726f7b8382351a0b38b4eabdd0b07a62d7acf70a1b6aedcb2e37b2929fdbf1db","target":"graph","created_at":"2026-07-05T09:46:15Z","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/2412.06113/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This survey comprehensively explores the landscape of privacy-preserving mechanisms tailored for LLMs, including differential privacy, federated learning, cryptographic protocols, and trusted execution environments. We examine their efficacy in add","authors_text":"Eric Song, Guoshenghui Zhao","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T00:24:09Z","title":"Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06113","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":"sha256:481a9a30a5a134e5d58bd4564c74c0a5729de8fd1706b82963f73cd387b64445","target":"record","created_at":"2026-07-05T09:46:15Z","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":"b133463786e7118dd1525fce1ea3d51b0e5c76df5a16e074d85e202fcb5be6ea","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-12-09T00:24:09Z","title_canon_sha256":"413da0388c3aebf933169cfe983ceb8d82916cfe3f4b70d658fe01964bcf3642"},"schema_version":"1.0","source":{"id":"2412.06113","kind":"arxiv","version":1}},"canonical_sha256":"900f57a3880b52e7e6322b287b92eedb0848f71eb34cc647b4911c8c9eaa8f74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"900f57a3880b52e7e6322b287b92eedb0848f71eb34cc647b4911c8c9eaa8f74","first_computed_at":"2026-07-05T09:46:15.239340Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:15.239340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BtjlpUbghGnhgEmaFaYL6D6VJfoKvkOz2tPLXR/RSe5tbxqOzA9eGP0R0NZj8RS+ms6nZa7gOo5XcwbnKFj4Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:15.239799Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.06113","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:481a9a30a5a134e5d58bd4564c74c0a5729de8fd1706b82963f73cd387b64445","sha256:726f7b8382351a0b38b4eabdd0b07a62d7acf70a1b6aedcb2e37b2929fdbf1db"],"state_sha256":"4fe33b28504151eaaa6bd1328aec3b6117b501952a8c37d47cbcbcf11d667349"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JEcq2mhtK3QRFehrobGOCFV8jxeAzo72ZnbjZ/zHVoFnHVgKG10zJ5gOr5RLl6copgjIsotpkK/Mqq0GcEBzAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:48:37.559449Z","bundle_sha256":"848d27819cfcbd142b490fc101c4af3767b188efdfe8835fbe81f0988034d95c"}}