Generating Artificial Data for Private Deep Learning

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate labelled data of high quality and use it to successfully train and validate supervised models. Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.


Published in:
Proceedings of the PAL: Privacy-Enhancing Artificial Intelligence and Language Technologies, AAAI Spring Symposium Series
Year:
2019
Laboratories:




 Record created 2019-08-14, last modified 2019-08-19

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