Zhang等人[12]通过设计基于对抗鲁棒性的成员推理攻击,进一步探讨了鲁棒性对于隐私的影响。 [1]R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks Against Machine Learning Models,”IEEE Symposium on Security and Privacy (SP), pp. 3–18, Oct. 2017, doi: 10.1109/SP...
成员推理攻击在开山之作中被这样解释:定义攻击模型fattack(),它的输入xattack是一个由正确的标签类和一个目标模型(被攻击模型)的预测置信度向量组成(后面我们会看到,其实不一定非得如此,这也是成员推理攻击的tricks之一),该攻击模型的输出为一个预测类"in"(member)或"out"(non-member)。说了这么多,其实它的数学...
Membership Inference Attack的防御方法 defense_methods 2024年12月14日更新 前面我们实现了多种模型的成员推理攻击,接下来我们将针对其防御方法及其原理进行一个简单的介绍,并进行简单的代码实现,给用户提供一个详细的帮助文档。 目录 基本介绍常用方法介绍简单实现 基本介绍 机器学习中的成员推理攻击指的是攻击者可以通...
Membership Inference Attack 2024年5月12日更新 在此教程中,我们将对成员推断攻击的定义及其原理进行一个简单的介绍,并实现成员推断攻击模型,目前支持数据集有:MNIST、fashionMNIST、CIFAR10等,同时给用户提供一个详细的说明和帮助文档。 目录 基本介绍 定义 核心思想 分类 影子模型 影子模型概述 生成影子模型的训练...
Membership inference is one of the simplest pri- vacy threats faced by machine learning models that are trained on private sensitive data. In this attack, an adversary infers whether a particular point was used to train the model, or not, by ob- serving the model's predictions. Whereas ...
论文笔记:Membership Inference Attacks Against Machine Learning Models,程序员大本营,技术文章内容聚合第一站。
Risks associated with input Inference Privacy Traditional AI risk Description Given a trained model and a data sample, an attacker appropriately samples the input space, observing outputs to deduce whether that sample was part of the model's training. This is known as a membership inference attack...
论文阅读 | NDSS19 | ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on ML,程序员大本营,技术文章内容聚合第一站。
Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism provides protection against this mode of attack while leading to negligible loss in downstream performances. In ...
We for the first time propose six membership inference attack (MIA) strategies tailored for LCLMs and conduct extensive experiments on various popular models. Empirical results demonstrate that our attacks can accurately infer membership status in most cases, e.g., 90.66% attack F1-score on Multi...