基地研究员熊平教授的论文“Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage”在重要学术期刊IEEE Transactions on Information Forensics and Security发表。

摘要：With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requested data and its influence from a trained model. Machine unlearning is an emerging technique designed to enable machine learning models to erase users’ private information. Although several efficient machine unlearning schemes have been proposed, these methods still have limitations. First, removing the contributions of partial data may lead to model performance degradation. Second, discrepancies between the original and generated unlearned models can be exploited by attackers to obtain target sample’s information, resulting in additional privacy leakage risks. To address above challenges, we proposed a game-theoretic machine unlearning algorithm that simulates the competitive relationship between unlearning performance and privacy protection. This algorithm comprises unlearning and privacy modules. The unlearning module possesses a loss function composed of model distance and classification error, which is used to derive the optimal strategy. The privacy module aims to make it difficult for an attacker to infer membership information from the unlearned data, thereby reducing the privacy leakage risk during the unlearning process. Additionally, the experimental results on real-world datasets demonstrate that this game-theoretic unlearning algorithm’s effectiveness and its ability to generate an unlearned model with a performance similar to that of the retrained one while mitigating extra privacy leakage risks.

关键词：Machine unlearning, membership inference attack, game theory

论文链接：https://ieeexplore.ieee.org/document/11208174

教师简介

熊平，中南财经政法大学教授，主持国家自然科学基金及省部级科研项目，发表学术论文90余篇（含SCI/EI检索40余篇），获“面向任务的面部隐私保护生成方法及系统”专利。2014年出版专著《信息安全原理及应用》，主讲《信息安全概论》《机器学习》等课程。2017年参与第六届全国网络与信息安全防护峰会并作学术交流。同年参与完成武汉市科技创新局项目，同年发表联邦学习隐私保护领域综述论文于《网络与信息安全学报》。