{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:04:34Z","timestamp":1780949074515,"version":"3.54.1"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402008"],"award-info":[{"award-number":["62402008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015549","name":"Macau Science and Technology Development Fund","doi-asserted-by":"publisher","award":["0131\/2025\/ITP2"],"award-info":[{"award-number":["0131\/2025\/ITP2"]}],"id":[{"id":"10.13039\/100015549","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tifs.2026.3698588","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:34:04Z","timestamp":1780342444000},"page":"5374-5387","source":"Crossref","is-referenced-by-count":0,"title":["Parameter-Agnostic Privacy-Preserving Machine Unlearning for Large Language Models"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-7910","authenticated-orcid":false,"given":"Lefeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3411-7947","authenticated-orcid":false,"given":"Tianqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Xie","sequence":"additional","affiliation":[{"name":"Hainan International College, Minzu University of China, Lingshui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-2132","authenticated-orcid":false,"given":"Binxing","family":"Fang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1680-2521","authenticated-orcid":false,"given":"Wanlei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"GPT-4 technical report","author":"Achiam","year":"2024","journal-title":"arXiv:2303.08774"},{"key":"ref2","article-title":"The Llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arxiv: 2407.21783"},{"key":"ref3","first-page":"23327","article-title":"Towards effective evaluations and comparisons for LLM unlearning methods","volume-title":"Proc. 30th Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2024.3403681"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-00985-0"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.107"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3346"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.375"},{"key":"ref10","first-page":"40034","article-title":"In-context unlearning: Language models as few-shot unlearners","volume-title":"Proc. 41st Interface Conf. Mach. Learn.","volume":"235","author":"Pawelczyk"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2025.104010"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i16.29784"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.805"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1116"},{"key":"ref16","first-page":"101772","article-title":"On large language model continual unlearning","volume-title":"Proc. 13th Int. Conf. Learn. Represent.","author":"Gao"},{"key":"ref17","article-title":"LoRA: low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2026.3660137"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2026.3672778"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i23.39017"},{"key":"ref21","article-title":"Offset unlearning for large language models","author":"Huang","year":"2024","journal-title":"arxiv: 2404.11045"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3754"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.174"},{"key":"ref24","article-title":"Guardrail baselines for unlearning in LLMs","volume-title":"Proc. ICLR Workshop Secure Trustworthy Large Lang. Models","author":"Thaker"},{"key":"ref25","article-title":"TOFU: A task of fictitious unlearning for LLMs","author":"Maini","year":"2024","journal-title":"arXiv:2401.06121"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3620832"},{"key":"ref27","article-title":"Resolving editing-unlearning conflicts: A knowledge codebook framework for large language model updating","author":"Zhang","year":"2025","journal-title":"arxiv: 2502.00158"},{"key":"ref28","article-title":"Class machine unlearning for complex data via concepts inference and data poisoning","author":"Chang","year":"2024","journal-title":"arxiv: 2405.15662"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2015.35"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3297905"},{"key":"ref31","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume-title":"Proc. NeurIPS","volume":"33","author":"Lewis"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531722"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.eacl-demo.16"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2554797.2554834"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3328269"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3028943"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939841"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1410"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3583231"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CSCWD61410.2024.10580849"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1525\/9780520940420-020"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1002\/9780470689646.ch1"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.560"},{"key":"ref46","article-title":"spaCy: Industrial-strength natural language processing in Python","author":"Honnibal","year":"2020"},{"key":"ref47","volume-title":"Natural Language Processing with Python: Analyzing Text With the Natural Language Toolkit","author":"Bird","year":"2009"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/2591796.2591826"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref50","article-title":"Eight methods to evaluate robust unlearning in LLMs","author":"Lynch","year":"2024","journal-title":"arxiv: 2402.16835"},{"key":"ref51","article-title":"Evaluating of machine unlearning: Robustness verification without prior modifications","author":"Xu","year":"2024","journal-title":"arxiv: 2410.10120"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3331239"},{"key":"ref53","first-page":"74","article-title":"Rouge: A package for automatic evaluation of summaries","volume-title":"Proc. Workshop Text Summarization ACL","author":"Lin"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/11313711\/11541209.pdf?arnumber=11541209","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:50:30Z","timestamp":1780948230000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11541209\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tifs.2026.3698588","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}