Generating Differentially Private Datasets Using GANsDownload PDF

15 Feb 2018 (modified: 10 Feb 2022)ICLR 2018 Conference Blind SubmissionReaders: Everyone
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
Data: [MNIST](https://paperswithcode.com/dataset/mnist), [SVHN](https://paperswithcode.com/dataset/svhn)
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