{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ABVDIMO4S5W7W32UTBGKEFIAN3","short_pith_number":"pith:ABVDIMO4","schema_version":"1.0","canonical_sha256":"006a3431dc976dfb6f54984ca215006ecf7c598193c1480cd3a64086e4716c50","source":{"kind":"arxiv","id":"2310.10383","version":2},"attestation_state":"computed","paper":{"title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CL","authors_text":"Bryan Hooi, Chunkit Chan, Hao Peng, Haoran Li, Jiecong Wang, Jinglong Luo, Qi Hu, Xiaojin Zhang, Yangqiu Song, Yan Kang, Yulin Chen, Zenglin Xu","submitted_at":"2023-10-16T13:23:54Z","abstract_excerpt":"The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful language models, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other hand, unrestricted access to these models can also introduce potential malicious and unintentional privacy risks. Despite ongoing efforts to address the safety and privacy concerns associated with LLMs, the problem remains unresolved. In this "},"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":"2310.10383","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-16T13:23:54Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"78f535f4848125aa5f362077ed549408a9c0ddfc54865df259777d99d00b96f2","abstract_canon_sha256":"ea1b2dc9fefd17a5f25a5e3bfbcffd9bada15e81bb599a5b66879a9aec8b25e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:33.831053Z","signature_b64":"BezzJNpoFFzAa67ie6m+Nz2i69A9OKQPvTzSQWwQr1y5qfcgPCtjABC264MxFluEPduR9gzjLg6QJ3TkWEDxDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"006a3431dc976dfb6f54984ca215006ecf7c598193c1480cd3a64086e4716c50","last_reissued_at":"2026-07-05T09:13:33.830556Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:33.830556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CL","authors_text":"Bryan Hooi, Chunkit Chan, Hao Peng, Haoran Li, Jiecong Wang, Jinglong Luo, Qi Hu, Xiaojin Zhang, Yangqiu Song, Yan Kang, Yulin Chen, Zenglin Xu","submitted_at":"2023-10-16T13:23:54Z","abstract_excerpt":"The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful language models, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other hand, unrestricted access to these models can also introduce potential malicious and unintentional privacy risks. Despite ongoing efforts to address the safety and privacy concerns associated with LLMs, the problem remains unresolved. In this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10383","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/2310.10383/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":"2310.10383","created_at":"2026-07-05T09:13:33.830613+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10383v2","created_at":"2026-07-05T09:13:33.830613+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10383","created_at":"2026-07-05T09:13:33.830613+00:00"},{"alias_kind":"pith_short_12","alias_value":"ABVDIMO4S5W7","created_at":"2026-07-05T09:13:33.830613+00:00"},{"alias_kind":"pith_short_16","alias_value":"ABVDIMO4S5W7W32U","created_at":"2026-07-05T09:13:33.830613+00:00"},{"alias_kind":"pith_short_8","alias_value":"ABVDIMO4","created_at":"2026-07-05T09:13:33.830613+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27302","citing_title":"AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31208","citing_title":"Probing Memorization of Tabular In-Context Learning","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2503.06223","citing_title":"RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2509.18127","citing_title":"Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2510.22628","citing_title":"Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23795","citing_title":"LLM-CEG: Extending the Classification Error Gauge Framework for Privacy Auditing of Large Language Models","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20720","citing_title":"COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05571","citing_title":"Understanding User Privacy Perceptions of GenAI Smartphones","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02255","citing_title":"On the Privacy of LLMs: An Ablation Study","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3","json":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3.json","graph_json":"https://pith.science/api/pith-number/ABVDIMO4S5W7W32UTBGKEFIAN3/graph.json","events_json":"https://pith.science/api/pith-number/ABVDIMO4S5W7W32UTBGKEFIAN3/events.json","paper":"https://pith.science/paper/ABVDIMO4"},"agent_actions":{"view_html":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3","download_json":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3.json","view_paper":"https://pith.science/paper/ABVDIMO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10383&json=true","fetch_graph":"https://pith.science/api/pith-number/ABVDIMO4S5W7W32UTBGKEFIAN3/graph.json","fetch_events":"https://pith.science/api/pith-number/ABVDIMO4S5W7W32UTBGKEFIAN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3/action/storage_attestation","attest_author":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3/action/author_attestation","sign_citation":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3/action/citation_signature","submit_replication":"https://pith.science/pith/ABVDIMO4S5W7W32UTBGKEFIAN3/action/replication_record"}},"created_at":"2026-07-05T09:13:33.830613+00:00","updated_at":"2026-07-05T09:13:33.830613+00:00"}