Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

TL;DR

This paper introduces a flexible framework using differentially private generative adversarial networks to produce high-quality, privacy-preserving open data across time series, continuous, and discrete data types, addressing a key challenge in data sharing.

Contribution

It presents a novel, adaptable framework for privacy-preserving data publishing that handles various data types, including discrete data, which was not addressed in prior work.

Findings

Effective generation of privacy-preserving data for multiple data types

Maintains original data distribution and feature correlations

Demonstrated on real-world datasets from public administration

Abstract

Open data plays a fundamental role in the 21th century by stimulating economic growth and by enabling more transparent and inclusive societies. However, it is always difficult to create new high-quality datasets with the required privacy guarantees for many use cases. This paper aims at creating a framework for releasing 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; no previous work has focused on the later class. Indeed, many use cases expose discrete data or at least a combination between categorical and numerical values. Thanks to the latest developments in deep learning and generative models, it is now possible to model rich-semantic data maintaining both the original distribution of the features and the correlations between them. The output of this framework is a deep network, namely a generator, able to create new data on demand. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets.