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Membership Inference Attack Against Principal Component Analysis

Abstract : This paper studies the performance of membership inference attacks against principal component analysis (PCA). In this attack, we assume that the adversary has access to the principal components, and her main goal is to infer whether a given data sample was used to compute these principal components. We show that our attack is successful and achieves high performance when the number of samples used to compute the principal components is small. As a defense strategy, we investigate the use of various differentially private mechanisms. Accordingly, we present experimental results on the performance of Gaussian and Laplace mechanisms under naive and advanced compositions against MIA as well as the utility of these differentially-private PCA solutions.
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Submitted on : Friday, September 16, 2022 - 1:50:45 PM
Last modification on : Friday, September 30, 2022 - 3:59:59 AM


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Oualid Zari, Javier Parra-Arnau, Ayşe Ünsal, Thorsten Strufe, Melek Önen. Membership Inference Attack Against Principal Component Analysis. PSD 2022, Privacy in Statistical Databases, Sep 2022, Paris, France. pp.269-282, ⟨10.1007/978-3-031-13945-1_19⟩. ⟨hal-03779050⟩



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