Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data
In this folder you can find an exhaustive explanation of the experiments that prove the flexibility and effectiveness of the DifferentiallyPrivate-GenerativeAdversarialNetwork framework. Our solution is able to release new open data while protecting the individuality of the users through a strict definition of privacy called differential privacy. Unlike previous work, this paper provides a framework for privacy preserving data publishing that can be easily adapted to different use cases, from the generation of time-series to continuous data, and discrete data.