【摘要】 海量数据和强大算力的出现,促进了深度学习的发展,深度学习在智慧交通和医疗诊断等领域得到广泛应用,这给人们的日常生活带来许多便利。然而,机器学习中的隐私泄露问题也不容忽视。成员推理攻击通过推断数据样本是否用于训练机器学习模型,从而暴露用户训练数据的隐私。文章介绍了基于单阈值的成员推理攻击及特点,对不同攻击方法的成员和非成员的数据分布进行可视化,然后对成员推理攻击成功的内在机理进行分析,提出了基于双阈值函数的攻击模型,并通过实验对单阈值和双阈值的成员推理攻击进行系统性的分析对比,分析基于阈值成员推理攻击对不同模型和不同数据集的攻击表现。通过对多组控制变量的对比实验表明,基于双阈值函数的成员推理攻击在某些数据集和模型上,整体表现更加优异和稳定。更多还原

【Abstract】 The emergence of massive data and powerful computing power has brought deep learning to an unprecedented height, and its wide application in areas such as intelligent transportation and medical diagnosis has brought many conveniences to people’s daily lives. However, privacy leakage in machine learning cannot be ignored. Among them, the membership inference attack infers that whether the data sample can used in the training set of the machine learning model, thus interfering with the user’s training data. Firstly, this paper introduced the single-threshold-based membership inference attack and its characteristics,visualized the data distribution of members and non-members for different attack methods,then analyzed the internal mechanism of the successful membership inference attack, and proposed an attack model based on a double-threshold function, and systematically analyzed and compared single-threshold and double-threshold membership inference attacks through experiments, and analyzed the attack performance of threshold-based membership inference attacks on different models and different datasets. The comparative experiments on multiple groups of control variables show that the membership inference attack based on the doublethreshold function has better performance on some data sets and models, and the overall performance is more stable.更多还原