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We focus on the\nbasic membership inference attack: given a data record and black-box access to\na model, determine if the record was in the model's training dataset. To\nperform membership inference against a target model, we make adversarial use of\nmachine learning and train our own inference model to recognize differences in\nthe target model's predictions on the inputs that it trained on versus the\ninputs that it did not train on.\n  We empirically evaluate our inference techniques on classification models\ntrained by commercial \"machine learning as a service\" providers such as Google\nand Amazon. Using realistic datasets and classification tasks, including a\nhospital discharge dataset whose membership is sensitive from the privacy\nperspective, we show that these models can be vulnerable to membership\ninference attacks. 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