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Deploying machine learning models in production may allow adversaries toinfer sensitive information about training data. There is a vast literatureanalyzing different types of inference risks, ranging from membership inferenceto reconstruction attacks. Inspired by the success of games (i.e.,probabilistic experiments) to study security properties in cryptography, someauthors describe privacy inference risks in machine learning using a similargame-based style. However, adversary capabilities and goals are often stated insubtly different ways from one presentation to the other, which makes it hardto relate and compose results. In this paper, we present a game-based frameworkto systematize the body of knowledge on privacy inference risks in machinelearning. We use this framework to (1) provide a unifying structure fordefinitions of inference risks, (2) formally establish known relations amongdefinitions, and (3) to uncover hitherto unknown relations that would have beendifficult to spot otherwise.

SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning