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CIS Seminar: “Synthetic Data: Anonymisation Groundhog Day”
December 7, 2021 at 12:00 PM - 1:00 PM
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.
Our evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words, we empirically show that synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymisation techniques.
Furthermore, in contrast to traditional anonymisation, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.
EPFL Switzerland, Spring Lab
Carmela Troncoso is an Assistant Professor at EPFL (Switzerland), where she heads the SPRING Lab. Her work focuses on analyzing, building, and deploying secure and privacy-preserving systems. Troncoso holds a Ph.D. in engineering from KULeuven. Her thesis, Design and Analysis Methods for Privacy Technologies, received the European Research Consortium for Informatics and Mathematics Security and Trust Management Best Ph.D. Thesis Award, and her work on privacy engineering received the CNIL-INRIA Privacy Protection Award in 2017. She has been named 40 under 40 in technology by Fortune in 2020.