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

• Published in: ICT Systems Security and Privacy Protection Vol. 562; pp. 151 - 164
• Main Authors: Frigerio, Lorenzo, de Oliveira, Anderson Santana, Gomez, Laurent, Duverger, Patrick
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: IFIP Advances in Information and Communication Technology
• Subjects: Differential privacy; Generative Adversarial Networks
• ISBN: 3030223116; 9783030223113
• ISSN: 1868-4238; 1868-422X; 1868-422X
• DOI: 10.1007/978-3-030-22312-0_11

Open data plays a fundamental role in the 21st 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. In this paper, we developed a differential privacy framework for privacy preserving data publishing using Generative Adversarial Networks. It can be easily adapted to different use cases, from the generation of time-series, to continuous, and discrete data. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets. Our results maintain both the original distribution of the features and the correlations among them, at the same time providing a good level of privacy.