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Training generative models from privatized data
March 4, 2024, 5:42 a.m. | Daria Reshetova, Wei-Ning Chen, Ayfer \"Ozg\"ur
cs.LG updates on arXiv.org arxiv.org
Abstract: Local differential privacy is a powerful method for privacy-preserving data collection. In this paper, we develop a framework for training Generative Adversarial Networks (GANs) on differentially privatized data. We show that entropic regularization of optimal transport - a popular regularization method in the literature that has often been leveraged for its computational benefits - enables the generator to learn the raw (unprivatized) data distribution even though it only has access to privatized samples. We prove …
abstract adversarial arxiv collection cs.cr cs.it cs.lg data data collection differential differential privacy framework gans generative generative adversarial networks generative models literature math.it networks paper popular privacy regularization show training transport type
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