Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data - IFIP Open Digital Library Access content directly
Conference Papers Year : 2019

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

Anderson Oliveira
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Laurent Gomez
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Patrick Duverger
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Abstract

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.
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hal-03744310 , version 1 (02-08-2022)

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Lorenzo Frigerio, Anderson Oliveira, Laurent Gomez, Patrick Duverger. Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data. 34th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Jun 2019, Lisbon, Portugal. pp.151-164, ⟨10.1007/978-3-030-22312-0_11⟩. ⟨hal-03744310⟩
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