Membership Inference Attack on Private Evolution
Daniil Filienko, Sikha Pentyala, Martine De Cock
Abstract
Synthetic Data Generation (SDG) is an actively studied way to enable safe sharing of private data more broadly. Inference-only SDG methods, such as Private Evolution (PE), are particularly appealing, as they can leverage black-box access to pre-trained foundation models to generate high quality data without the need for model fine-tuning on the private data. PE is explicitly designed to produce synthetic data under Differential Privacy (DP). Yet formal DP guarantees do not eliminate the need for empirical privacy auditing of the practical susceptibility of released synthetic data to adversarial attacks. In this work, we introduce the first white-box Membership Inference Attack (MIA) tailored to the voting-based algorithm underlying the PE framework. By exploiting how the iterative filtering process biases the survival and proximity of synthetic candidates toward private instances, our attack achieves empirical performance indicating private data leakage in the PE applications across image, text, and tabular modalities.