A Method to Facilitate Membership Inference Attacks in Deep Learning Models

Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and code repositories that can greatly facilitate the development of ML models.Today, even ordinary data holders who are not ML experts can apply an offthe-shelf codebase to build high-performance ML models on their data, which are often sensitive in nature (e.g., clinical records).In this work, we consider a malicious ML provider who supplies model-training code to the data holders, does not have access to the training process, and has only black-box query access to the resulting model.In this setting, we demonstrate a new form of membership inference attack that is strictly more powerful than prior art.Our attack empowers the adversary to reliably de-identify all the training samples (average >99% attack [email protected]% FPR).Further, the compromised models still maintain competitive performance as their uncorrupted counterparts (average <1% accuracy drop).Finally, we show that the poisoned models can effectively disguise the amplified membership leakage under common membership privacy auditing, which can only be revealed by a set of secret samples known by the adversary.Overall, our study not only points to the worst-case membership privacy leakage, but also unveils a common pitfall underlying existing privacy auditing methods.Thus, our work is a call-toarms for future efforts to rethink the current practice of auditing membership privacy in machine learning models 1 .