Differential privacy is a promising approach to the privacy-preserving release of data: it offers a strong guaranteed bound on the increase in harm that a user incurs as a result of participating in a differentially private data analysis. Several mechanisms and software tools have been developed to ensure differential privacy for a wide range of data analysis tasks, such as combinatorial optimization, machine learning, answering distributed queries, etc.
Researchers in differential privacy come from several area of computer science as algorithms, programming languages, security, databases, machine learning, as well as from several areas of statistics and data analysis. The workshop is intended to be an occasion for researchers from these different research areas to discuss the recent developments in the theory and practice of differential privacy.
Since the first TPDP workshop, the Journal of Privacy and Confidentiality has published a special issue of selected and expanded workshop papers from each workshop, under the editorial supervision of one of the TPDP organizers. Submissions should use the JPC Submissions Page, and mention the workshop title in the comments to the editor. All submissions are peer-reviewed.