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arXiv · 2504.00758

TAMIS: Tailored Membership Inference Attacks on Synthetic Data

Paul Andrey·Batiste Le Bars·Marc Tommasi
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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.

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Paul Andrey, Batiste Le Bars, Marc Tommasi. 2025-04-01. TAMIS: Tailored Membership Inference Attacks on Synthetic Data. https://doi.org/10.1007/978-3-032-06096-9_12

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