Leveraging Adversarial Examples to Quantify Membership Information Leakage

• URL: http://arxiv.org/abs/2203.09566v1
• Date: Thu, 17 Mar 2022 19:09:38 GMT
• Title: Leveraging Adversarial Examples to Quantify Membership Information Leakage
• Authors: Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi and Pablo Piantanida
• Abstract summary: We develop a novel approach to address the problem of membership inference in pattern recognition models. We argue that this quantity reflects the likelihood of belonging to the training data. Our method performs comparable or even outperforms state-of-the-art strategies.
• Score: 30.55736840515317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 machine learning today. Identifying training data based on a trained model is a standard way of measuring the privacy risks induced by the model. We develop a novel approach 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.

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