TPDP 2015 - 1st Theory and Practice of Differential Privacy (TPDP)

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.
Specific topics of interest for the workshop include (but are not limited to):
theory of differential privacy,
verification techniques for differential privacy,
programming languages for differential privacy,
models for differential privacy,
trade-offs between privacy protection and analytic utility,
differential privacy and surveys,
relaxations of the differential privacy definition,
differential privacy vs other privacy notions and methods,
differential privacy and accuracy,
practical differential privacy,
implementations for differential privacy,
differential privacy and security,
applications of differential privacy.
Organisers:
Gilles Barthe, IMDEA Software (gjbarthe-AT-gmail.com)
George Danezis, University College London (g.danezis-AT-ucl.ac.uk)
Marco Gaboardi, University of Dundee (m.gaboardi-AT-dundee.ac.uk)
Aaron Roth, University of Pennsylvania (aaroth-AT-cis.upenn.edu)
C Skinner, London School of Economics (C.J.Skinner-AT-lse.ac.uk)