Membership Inference Attacks on Machine Learning Model

Abstract

Machine learning(ML) models today are vulnerable to several types of attacks. In this work, we will study a category of attack known as membership inference attack and show how ML models are susceptible to leaking secure information under such attacks. Given a data record and a black box access to a ML model, we present a framework to deduce whether the data record was part of the model’s training dataset or not. We achieve this objective by creating an attack ML model which learns to differentiate the target model’s predictions on its training data from target model’s predictions on data not part of its training data. In other words, we solve this membership inference problem by converting it into a binary classification problem. We also study mitigation strategies to defend the ML models against the attacks discussed in this work. In this paper evaluation method on real world datasets: (1) CIFAR-10 and (2) UCI Adult (Census Income) using classification as the task performed by the target ML models built on these datasets.