Active forgetting via influence estimation for neural networks

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 9080-9107 RESEARCH ARTICLE Active forgetting via influence estimation for neural networks Xianjia Meng, Xianjia Meng School of Information Science and Technology, Northwest University, Xi'an, People's Republic of ChinaSearch for more papers by this authorYong Yang, Yong Yang School of Information Science and Technology, Northwest University, Xi'an, People's Republic of ChinaSearch for more papers by this authorXimeng Liu, Corresponding Author Ximeng Liu snbnix@gmail.com orcid.org/0000-0002-4238-3295 Fujian Province Key Laboratory of Information Security of Network System, and College of Computer and Data Science, Fuzhou University, Fuzhou, People's Republic of China Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, People's Republic of China Correspondence Ximeng Liu, Fujian Province Key Laboratory of Information Security of Network System, and College of Computer and Data Science, Fuzhou University, Fuzhou, People's Republic of China. Email: snbnix@gmail.comSearch for more papers by this authorNan Jiang, Nan Jiang Department of Internet of Things, School of Information Engineering, East China Jiao Tong University, Nanchang, People's Republic of ChinaSearch for more papers by this author Xianjia Meng, Xianjia Meng School of Information Science and Technology, Northwest University, Xi'an, People's Republic of ChinaSearch for more papers by this authorYong Yang, Yong Yang School of Information Science and Technology, Northwest University, Xi'an, People's Republic of ChinaSearch for more papers by this authorXimeng Liu, Corresponding Author Ximeng Liu snbnix@gmail.com orcid.org/0000-0002-4238-3295 Fujian Province Key Laboratory of Information Security of Network System, and College of Computer and Data Science, Fuzhou University, Fuzhou, People's Republic of China Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, People's Republic of China Correspondence Ximeng Liu, Fujian Province Key Laboratory of Information Security of Network System, and College of Computer and Data Science, Fuzhou University, Fuzhou, People's Republic of China. Email: snbnix@gmail.comSearch for more papers by this authorNan Jiang, Nan Jiang Department of Internet of Things, School of Information Engineering, East China Jiao Tong University, Nanchang, People's Republic of ChinaSearch for more papers by this author First published: 05 August 2022 https://doi.org/10.1002/int.22981Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The rapidly exploding of user data, especially applications of neural networks, involves analyzing data collected from individuals, which brings convenience to life. Meanwhile, privacy leakage in the applications as a potential threat needs to be addressed urgently. However, removing private information from models is difficult once the user's sensitive data enters machine learning models, particularly neural networks. Most of the previous amnestic methods based on retraining require full access to the training set of the target model and have limited improvements in computational resources and time improvement. In this paper, we propose Scrubber, which removes sensitive data from the original model via influence estimation to produce an unlearning model that is approximately indistinguishable from the retrained model. S crubber builds on the essential concept of influence function and reformulates the influence estimation as a closed-form update of forgetting. For learned models with strictly convex loss functions, our approach theoretically guarantees the effectiveness of forgetting while empirically demonstrating forgetting performance. For models with non-convex losses, we relax strictly convex assumptions by applying a damping term that allows us to make approximate estimates with negligible errors from the original assumption. Furthermore, experiments show that S crubber only causes less than 1% and 3% accuracy drop with more than 80% forgetting rate on average for logistic regression models and convolutional neural networks. The accuracy drop is reduced by 2%–3% compared to most state-of-the-art methods. Open Research DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no new data were created or analyzed in this study. Volume37, Issue11November 2022Pages 9080-9107 RelatedInformation