计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 302-317.doi: 10.11896/jsjkx.220800227
陈得鹏1, 刘肖1, 崔杰1, 何道敬2
CHEN Depeng1, LIU Xiao1, CUI Jie1, HE Daojing2
摘要： 随着机器学习的不断发展,特别是在深度学习领域,人工智能已经融入到人们日常生活的方方面面。机器学习模型被部署到多种场景的应用中,提升了传统应用的智能化水平。然而,近年来的研究指出,用于训练机器学习模型的个人数据时常面临隐私泄露的风险。其中,成员推理攻击就是针对机器学习模型威胁用户隐私安全的一种非常重要的攻击方式。成员推理攻击的目的是判断用户数据样本是否被用于训练目标模型(如在医疗、金融等领域的用户数据),从而直接干涉到用户隐私信息。首先介绍了成员推理攻击的相关背景知识,随后对现有的成员推理攻击按照攻击者是否拥有影子模型进行分类,并对成员推理攻击在不同领域的威胁进行了相应的总结。其次,介绍了应对成员推理攻击的防御手段,对现有的防御机制按照模型过拟合、基于模型压缩和基于扰动等策略进行分类和总结。最后,对现有的成员推理攻击和防御机制的优缺点进行了分析,并提出了成员推理攻击的一些潜在的研究方向。
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