Title: Privacy-Preserving Adversarial Networks,
Abstract: We propose a data-driven framework for optimizing privacy-preserving data
release mechanisms toward the information-theoretically optimal tradeoff
between minimizing distortion of useful data and concealing sensitive
information. Our approach employs adversarially-trained neural networks to
implement randomized mechanisms and to perform a variational approximation of
mutual information privacy. We empirically validate our Privacy-Preserving
Adversarial Networks (PPAN) framework with experiments conducted on discrete
and continuous synthetic data, as well as the MNIST handwritten digits dataset.
With the synthetic data, we find that our model-agnostic PPAN approach achieves
tradeoff points very close to the optimal tradeoffs that are
analytically-derived from model knowledge. In experiments with the MNIST data,
we visually demonstrate a learned tradeoff between minimizing the pixel-level
distortion versus concealing the written digit.