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Communication Dans Un Congrès Année : 2022

Leveraging Adversarial Examples to Quantify Membership Information Leakage

Résumé

The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in ma- chine learning today. Identifying training data based on a trained model is a standard way of measuring the pri- vacy risks induced by the model. We develop a novel ap- proach to address the problem of membership inference in pattern recognition models, relying on information provided by adversarial examples. The strategy we propose consists of measuring the magnitude of a perturbation necessary to build an adversarial example. Indeed, we argue that this quantity reflects the likelihood of belonging to the training data. Extensive numerical experiments on multivariate data and an array of state-of-the-art target models show that our method performs comparable or even outperforms state- of-the-art strategies, but without requiring any additional training samples.

Dates et versions

hal-03919891 , version 1 (03-01-2023)

Identifiants

Citer

Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida. Leveraging Adversarial Examples to Quantify Membership Information Leakage. CVPR 2022 - IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2022, New Orleans, United States. pp.10389-10399, ⟨10.1109/CVPR52688.2022.01015⟩. ⟨hal-03919891⟩
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