Yves-Alexandre de Montjoye

Matthieu Meeus, Florent Guépin, Ana-Maria Cretu et al.

Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record's closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data.

Euodia Dodd, Nataša Krčo, Igor Shilov et al.

Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attacks require training numerous, often computationally expensive, reference models, limiting their practicality. We present a novel approach for estimating model-level vulnerability, the TPR at low FPR, to membership inference attacks without requiring reference models. Empirical analysis shows loss distributions to be asymmetric and heavy-tailed and suggests that most points at risk from MIAs have moved from the tail (high-loss region) to the head (low-loss region) of the distribution after training. We leverage this insight to propose a method to estimate model-level vulnerability from the training and testing distribution alone: using the absence of outliers from the high-loss region as a predictor of the risk. We evaluate our method, the TNR of a simple loss attack, across a wide range of architectures and datasets and show it to accurately estimate model-level vulnerability to the SOTA MIA attack (LiRA). We also show our method to outperform both low-cost (few reference models) attacks such as RMIA and other measures of distribution difference. We finally evaluate the use of non-linear functions to evaluate risk and show the approach to be promising to evaluate the risk in large-language models.

Ana-Maria Creţu, Florent Guépin, Yves-Alexandre de Montjoye

Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset. We first propose a model-less attack, where an adversary exploits the spherical parametrization of correlation matrices alone to make an informed guess. Second, we propose a model-based attack, where an adversary exploits black-box model access to infer the correlations using minimal and realistic assumptions. Third, we evaluate our attacks against logistic regression and multilayer perceptron models on three tabular datasets and show the models to leak correlations. We finally show how extracted correlations can be used as building blocks for attribute inference attacks and enable weaker adversaries. Our results raise fundamental questions on what a model does and should remember from its training set.