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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.","url_abs":"http://arxiv.org/abs/1901.02477v2","url_pdf":"http://arxiv.org/pdf/1901.02477v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"differentially-private-generative-adversarial","repo_url":"https://github.com/Lory94/dp-GAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"differentially-private-generative-adversarial","repo_url":"https://github.com/SAP-samples/security-research-differentially-private-generative-models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.02477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02477"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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