Title: TAMIS: Tailored Membership Inference Attacks on Synthetic Data Show Chinese title

Title: 针对合成数据的定制成员推理攻击：TAMIS

Abstract: Membership Inference Attacks (MIA) enable to empirically assess the privacy of a machine learning algorithm. In this paper, we propose TAMIS, a novel MIA against differentially-private synthetic data generation methods that rely on graphical models. This attack builds upon MAMA-MIA, a recently-published state-of-the-art method. It lowers its computational cost and requires less attacker knowledge. Our attack is the product of a two-fold improvement. First, we recover the graphical model having generated a synthetic dataset by using solely that dataset, rather than shadow-modeling over an auxiliary one. This proves less costly and more performant. Second, we introduce a more mathematically-grounded attack score, that provides a natural threshold for binary predictions. In our experiments, TAMIS achieves better or similar performance as MAMA-MIA on replicas of the SNAKE challenge. Show Chinese abstract

Abstract: 成员推理攻击（MIA）能够经验性地评估机器学习算法的隐私性。 本文提出了一种新的针对基于图模型的差分隐私合成数据生成方法的MIA——TAMIS。 该攻击基于最近发表的最先进的MAMA-MIA方法。 它降低了计算成本，并且需要攻击者掌握更少的知识。 我们的攻击通过两方面的改进实现。 首先，我们仅使用合成数据集本身恢复了生成该数据集的图模型，而不是在辅助数据集上进行影子建模。 这证明成本更低且性能更高。 其次，我们引入了一个更具数学基础的攻击评分，为二元预测提供了自然阈值。 在我们的实验中，TAMIS在SNAKE挑战复制品上的表现优于或相当于MAMA-MIA。