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Machine learning (ML) has become a core component of many real-worldapplications and training data is a key factor that drives current progress.This huge success has led Internet companies to deploy machine learning as aservice (MLaaS). Recently, the first membership inference attack has shown thatextraction of information on the training set is possible in such MLaaSsettings, which has severe security and privacy implications.  However, the early demonstrations of the feasibility of such attacks havemany assumptions on the adversary, such as using multiple so-called shadowmodels, knowledge of the target model structure, and having a dataset from thesame distribution as the target model's training data. We relax all these keyassumptions, thereby showing that such attacks are very broadly applicable atlow cost and thereby pose a more severe risk than previously thought. Wepresent the most comprehensive study so far on this emerging and developingthreat using eight diverse datasets which show the viability of the proposedattacks across domains.  In addition, we propose the first effective defense mechanisms against suchbroader class of membership inference attacks that maintain a high level ofutility of the ML model.

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models