TPDP 2016 – Theory and Practice of Differential Privacy (original) (raw)

Context

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.

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.

Submission

The overall 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 (2-4 pages maximum) of their work by May 1, 2016. Abstracts must be written in English and be submitted as a single PDF file at EasyChair page for TPDP.

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

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

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

• theory of differential privacy,
• privacy preserving machine learning,
• differential privacy and statistics,
• differential privacy and security,
• 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.

Call for Papers: txt