{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:06:27Z","timestamp":1769184387974,"version":"3.49.0"},"reference-count":29,"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:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3653817","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T20:59:51Z","timestamp":1768337991000},"page":"9272-9283","source":"Crossref","is-referenced-by-count":0,"title":["MiniUn: A Machine Unlearning Method to Minimize Dependency on Original Training Data"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9048-0995","authenticated-orcid":false,"given":"Soyoung","family":"Youn","sequence":"first","affiliation":[{"name":"Department of IT Engineering, Sookmyung Women&#x2019;s University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6471-5334","authenticated-orcid":false,"given":"Chulyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of IT Engineering, Sookmyung Women&#x2019;s University, Seoul, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Privacy-preserving machine learning: Methods, challenges and directions","author":"Xu","year":"2021","journal-title":"arXiv:2108.04417"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3211013"},{"key":"ref4","first-page":"539","article-title":"Update on the California consumer privacy act and other states\u2019 actions","volume":"77","author":"Shatz","year":"2022","journal-title":"Bus. Lawyer"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2312913"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2015.35"},{"key":"ref7","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sekhari"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2023.23087"},{"key":"ref9","first-page":"1","article-title":"Certified data removal from machine learning models","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","author":"Guo"},{"key":"ref10","first-page":"4126","article-title":"Machine unlearning via algorithmic stability","volume-title":"Proc. Conf. Learn. Theory","author":"Ullah"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00932"},{"key":"ref12","article-title":"SalUn: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation","author":"Fan","year":"2023","journal-title":"arXiv:2310.12508"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i11.29092"},{"key":"ref14","author":"Krizhevsky","year":"2009","journal-title":"Learning Multiple Layers of Features From Tiny Images"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref16","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref17","article-title":"Knowledge unlearning for mitigating privacy risks in language models","author":"Jang","year":"2022","journal-title":"arXiv:2210.01504"},{"key":"ref18","first-page":"27591","article-title":"Quark: Controllable text generation with reinforced unlearning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lu"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.740"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.457"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2024.3412742"},{"key":"ref22","article-title":"Forget vectors at play: Universal input perturbations driving machine unlearning in image classification","author":"Sun","year":"2024","journal-title":"arXiv:2412.16780"},{"key":"ref23","article-title":"Machine unlearning for image-to-image generative models","author":"Li","year":"2024","journal-title":"arXiv:2402.00351"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00874"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3265506"},{"key":"ref26","article-title":"Score forgetting distillation: A swift, data-free method for machine unlearning in diffusion models","author":"Chen","year":"2024","journal-title":"arXiv:2409.11219"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28996"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00874"},{"key":"ref29","author":"Mordvintsev","year":"2015","journal-title":"Inceptionism: Going Deeper Into Neural Networks"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11348058.pdf?arnumber=11348058","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:03:18Z","timestamp":1769115798000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11348058\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3653817","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}