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Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but, at the same time, provides perfect protection against privacy attacks. In this work, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques.
Sabine Süsstrunk, Raphaël Wan-Li Marc Achddou, Liying Lu
Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova