Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society(2021)
摘要
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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关键词
differential privacy,density estimation,normalizing flows,anomaly detection
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