Black-Box Membership Inference Attacks Against Contrastive Learning via Aggressive Data Augmentations

Summary

ADA-MIA is a black-box membership inference attack against contrastive learning (CL) encoders. It needs only an auxiliary dataset of training samples, without their membership labels, and it constructs membership features from aggressively augmented views of the target sample, enabling unsupervised inference. Across four datasets, it matches the performance of seven existing attacks while requiring only black-box embedding access to the encoder.

Topics

• Training data and privacy

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