论文标题
高斯机制下的对抗分类：校准攻击以敏感
Adversarial Classification under Gaussian Mechanism: Calibrating the Attack to Sensitivity
论文作者
论文摘要
这项工作使用统计学和信息理论工具研究了在差异隐私（DP）下使用高斯扰动的异常检测。在我们的环境中，对手旨在通过插入其他数据来修改统计数据集的内容，而无需通过使用DP保证来为自己的利益检测到。为此，我们表征了对手攻击的一阶统计和二阶统计信息的信息理论和统计阈值，这平衡了隐私预算和攻击的影响以保持未被发现。此外，我们基于Chernoff信息引入了一个新的隐私指标，以将差异隐私的对手分类为$（ε，δ） - $和Kullback-Leibler DP的更强替代品，用于高斯机制。分析结果由数值评估支持。
This work studies anomaly detection under differential privacy (DP) with Gaussian perturbation using both statistical and information-theoretic tools. In our setting, the adversary aims to modify the content of a statistical dataset by inserting additional data without being detected by using the DP guarantee to her own benefit. To this end, we characterize information-theoretic and statistical thresholds for the first and second-order statistics of the adversary's attack, which balances the privacy budget and the impact of the attack in order to remain undetected. Additionally, we introduce a new privacy metric based on Chernoff information for classifying adversaries under differential privacy as a stronger alternative to $(ε, δ)-$ and Kullback-Leibler DP for the Gaussian mechanism. Analytical results are supported by numerical evaluations.
