Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation

• URL: http://arxiv.org/abs/2103.14068v1
• Date: Thu, 25 Mar 2021 18:39:51 GMT
• Title: Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
• Authors: Chris Waites and Rachel Cummings
• Abstract summary: We propose the use of normalizing flow models that provide explicit differential privacy guarantees. We show how our algorithm can be applied to the task of differentially private anomaly detection.
• Score: 10.561489862855334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density evaluation. However, in contexts where individuals are directly associated with the training data, releasing such a model raises privacy concerns. In this work, we propose the use of normalizing flow models that provide explicit differential privacy guarantees as a novel approach to the problem of privacy-preserving density estimation. We evaluate the efficacy of our approach empirically using benchmark datasets, and we demonstrate that our method substantially outperforms previous state-of-the-art approaches. We additionally show how our algorithm can be applied to the task of differentially private anomaly detection.

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