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Personal data collected at scale promises to improve decision-making andaccelerate innovation. However, sharing and using such data raises seriousprivacy concerns. A promising solution is to produce synthetic data, artificialrecords to share instead of real data. Since synthetic records are not linkedto real persons, this intuitively prevents classical re-identification attacks.However, this is insufficient to protect privacy. We here present TAPAS, atoolbox of attacks to evaluate synthetic data privacy under a wide range ofscenarios. These attacks include generalizations of prior works and novelattacks. We also introduce a general framework for reasoning about privacythreats to synthetic data and showcase TAPAS on several examples.

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data