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Recently, the first membership inference attack has shown that\nextraction of information on the training set is possible in such MLaaS\nsettings, which has severe security and privacy implications.\n  However, the early demonstrations of the feasibility of such attacks have\nmany assumptions on the adversary, such as using multiple so-called shadow\nmodels, knowledge of the target model structure, and having a dataset from the\nsame distribution as the target model's training data. We relax all these key\nassumptions, thereby showing that such attacks are very broadly applicable at\nlow cost and thereby pose a more severe risk than previously thought. 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