Differential privacy is a promising approach to privacy-preserving data analysis, providing strong worst-case guarantees about the harm that a user could suffer from participating in a differentially private data analysis, while being flexible enough to allow for a wide variety of data analyses to be performed with a high degree of utility.
Researchers in differential privacy span many distinct research communities, including algorithms, computer security, cryptography, databases, data mining, machine learning, statistics, programming languages, social sciences, and law. The annual Theory and Practice of Differential Privacy workshop (TPDP) brings together researchers from these diverse communities.
Starting with TPDP 2015, the Journal of Privacy and Confidentiality publishes 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.
For TPDP-related issues of the Journal of Privacy and Confidentiality, see https://journalprivacyconfidentiality.org/index.php/jpc/tpdp