  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.

Cryptography and Security;Machine Learning