Dec 19, 2017 · We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff.

Our approach employs adversarially-trained neural networks to implement randomized mechanisms and to perform a variational approximation of mutual information ...

In our paper, we propose a privacy-preserving Generative Adversarial Network (pGAN), which can generate synthetic data of high quality, while preserving the ...

The main idea of this model was to use mutual information as the privacy measure and adversarial training of two deep neural networks, one as the mechanism and ...

The Privacy-Preserving Adversarial Networks (PPAN) framework is validated via proof-of-concept experiments on discrete and continuous synthetic data, ...

Oct 18, 2019 · Our approach employs adversarially-trained neural networks to implement randomized mechanisms and to perform a variational approximation of ...

Missing: Networks. | Show results with:Networks.

We propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to ...

Our framework guarantees that it does not store any information while processing. Our main goal is to protect personal information from the image data. After ...

The adversarial features rather than the original features are stored in the server's database to prevent leaked features from exposing facial information.