TPDP 2023 - Theory and Practice of Differential Privacy

Context

Differential privacy (DP) is the leading framework for data analysis with rigorous privacy guarantees. In the last 15 years, it has transitioned from the realm of pure theory to large scale, real world deployments. The use of differential privacy by the U.S. Census Bureau and increasing industry adoption has increased its public profile and presents new questions for social scientists and policymakers.

Differential privacy is an inherently interdisciplinary field, drawing researchers from a variety of academic communities including machine learning, statistics, security, theoretical computer science, databases, and law. The combined effort across a broad spectrum of computer science is essential for differential privacy to realize its full potential. To this end, this workshop aims to stimulate discussion among participants about both the state-of-the-art in differential privacy and the future challenges that must be addressed to make differential privacy more practical.

Specific topics of interest for the workshop include (but are not limited to):

• theory of differential privacy,
• differential privacy and security,
• privacy preserving machine learning,
• differential privacy and statistics,
• differential privacy and data analysis,
• trade-offs between privacy protection and analytic utility,
• differential privacy and surveys,
• programming languages for differential privacy,
• relaxations of the differential privacy definition,
• differential privacy vs other privacy notions and methods,
• experimental studies using differential privacy,
• differential privacy implementations,
• differential privacy and policy making,
• applications of differential privacy.
• reconstruction attacks and memorization

Submission

The goal of TPDP is to stimulate the discussion on the relevance of differentially private data analyses in practice. For this reason, we seek contributions from different research areas of computer science and statistics.

Authors are invited to submit a short abstract (4 pages maximum, with unlimited references and appendices (only read at reviewer's discretion)) of their work. Submissions are single-blind (non-anonymized), and there is no prescribed style file (though authors should be considerate of reviewers in their selection).

Submissions will undergo a lightweight review process and will be judged on originality, relevance, interest and clarity. Submission should describe novel work or work that has already appeared elsewhere but that can stimulate the discussion between different communities at the workshop. Accepted abstracts will be presented at the workshop either as a talk or a poster.

The workshop will not have formal proceedings and is not intended to preclude later publication at another venue.

Selected papers from the workshop will be invited to submit a full version of their work for publication in a special issue of the Journal of Privacy and Confidentiality.

Call for Papers: PDF