This workshop is jointly sponsored by:
An ever-increasing amount of data is available in digital form, often accessible via a network. Not surprisingly, this trend is accompanied by an increase in public awareness of privacy issues and by legislation of privacy laws. The interest in privacy, and the tension between privacy and utility of data, is amplified by our growing ability to collect and store large amounts of data, and our ability to mine meaningful information from it. This workshop will view privacy in a broad sense in order to facilitate interaction and discussion between privacy-oriented researchers in different communities.
The study of "privacy" is inherently interdisciplinary, spanning a range of applications and scenarios, such as analysis of census data, detection and prevention of terrorist activity, and biomedical research. There is a fundamental interplay between privacy and law, security, economics, and the social sciences. This workshop will foster interactions between researchers in these fields with those in statistics and computer science, toward the goal of developing problem formulations that can be translated into a technical mathematical language that lends itself to a more rigorous study of privacy. The workshop will contrast these formal definitions with more intuitive notions of privacy from the social sciences, economics, philosophy and law to determine the extent to which they capture the perceived meaning of privacy in different settings.
Privacy-preserving technologies may soon become an integral part of the basic infrastructure for the collection and dissemination of official statistics, as well as for research in business, economics, medical sciences, and social sciences. Functional solutions for preserving privacy would therefore serve as a central part of the infrastructure for those disciplines. This workshop will address a variety of questions on algorithms for privacy-preserving analysis such as: