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...
Membership inference attack目标是确定一个样本是否被用于训练机器学习模型,能够引发严重的隐私安全问题。相关的隐私攻击有模型提取攻击,属性推断攻击,特性推断攻击和成员推理攻击。本文总结了各种成员推断攻击以及防御方法。 TYPES OF MEMBERSHIP INFERENCE ATTACKS 根据敌手的知识,成员推理攻击可以分为黑盒和白盒攻击。 敌手...
首先是一组参数(字典)的输出,这些参数定义了模型训练的配置: 其中target_model: 目标模型(例如CNN);target_learning_rate: 目标模型的学习率;target_epochs: 目标模型训练的轮数;n_shadow: 阴影模型的数量;attack_model: 攻击模型(例如FC,全连接模型);attack_epochs: 攻击模型训练的轮数,等等。 接着开始训练目标...
One of the most common privacy threats is the membership inference attack (MIA). Existing MIAs can effectively explore the potential privacy leakage risks of deep neural networks. However, DNNs are usually compressed for practical use, especially for edge computing, MIA will fail due to changes ...
Nevertheless, the current white-box attack is challenging to work when there are more than four participants in FL. In this paper, we propose CS-MIA, prediction Confidence Series based Membership Inference Attack in FL, which poses a more serious threat to participants’ privacy. The inspirations...
Keywords:Membership Inference Attack,Adversarial Example Takeaways: This paper proposed a fancy idea ofdefending MIA attacks by leveraging AE attack to ATTACK attackers. The defense outperforms counterparts by strictlybounding the utility-lossof confidence score vector, thus achieving optimal trade-off bet...
(2) Since the literature during the period of 2017∼2021 only covers 27% of the landscape (in terms of how to categorize), the number of new insights (i.e., why an MI attack could succeed) behind all the proposed MI attacks has been significantly increasing since year 2021. As a ...
A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model’s training dataset. These attacks are currently evaluated using average-case “accuracy” metrics that fail to characterize whether the...
Membership inference is a widely-studied class of threats against Machine Learning (ML) models. The goal of a membership inference attack is to infer whether a given record was used to train a specific ML model. An attacker might have full access to the model and its weights (known as "wh...
We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision using just \emph{a single query}, where the target model only returns the predicted hard label. This is in ...