Generating Differentially Private Datasets Using GANs

Aleksei Triastcyn, Boi Faltings

Feb 15, 2018 (modified: Oct 27, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data. We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gradients differentially private with respect to the training data, and then use the generator component to synthesise privacy-preserving artificial dataset. Our experiments show that under a reasonably small privacy budget we are able to generate data of high quality and successfully train machine learning models on this artificial data.
  • TL;DR: Train GANs with differential privacy to generate artificial privacy-preserving datasets.
  • Keywords: generative adversarial networks, differential privacy, synthetic data
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