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Statistical and Learning-Theoretic Challenges in Data Privacy
February 22 - 26, 2010
Organizing Committee |
Scientific Overview |
Speaker List
Application/Registration |
Contact Us
Organizing Committee
Adam Smith, Chair
(Pennsylvania State University)
Cynthia Dwork
(Microsoft Research, Research)
Stephen Fienberg
(Carnegie-Mellon University)
Aleksandra Slavkovic
(Pennsylvania State University)
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Scientific Overview
Privacy is a fundamental problem in modern data analysis. Collections of personal and
sensitive data, previously the purview of governments and statistical agencies, have become
ubiquitous. Increasing volumes of personal and sensitive data are collected and archived by health
networks, government agencies, search engines, social networking websites, and other organizations.
The potential social benefits of analyzing these databases are significant: better informed policy
decisions, more efficient markets, and more accurate public health data, just to name a few. At the
same time, releasing information from repositories of sensitive data can cause devastating damage to
the privacy of individuals or organizations whose information is stored there. The challenge is to
enable analysis of these databases, without compromising the privacy of the individuals whose data
they contain. This problem is studied in several scientific communities and under several names,
e.g. `statistical disclosure limitation' `privacy-preserving data mining', and "private data analysis".
The goal of workshop is to establish a coherent theoretical foundation for research on data privacy.
This implies work on (1) how the conflicting goals of privacy and utility can or should be formulated
mathematically; and (2) how the constraints of privacy---in their various incarnations---affect the
accuracy of statistical inference and machine learning. In particular, the goal is to shed light on
the interplay between privacy and concepts such as consistency and efficiency of estimators,
generalization error of learning, robustness and stability of estimation algorithms, and the generation
of synthetic data.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
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Confirmed Speakers
John Abowd
(Cornell University)
Khamalika Chaudhuri
(University of San Diego)
Cynthia Dwork
(Microsoft Research)
Stephen Fienberg
(Carnegie-Mellon University)
Jiashun Jin
(Carnegie-Mellon University)
Daniel Kifer
(Pennsylvania State University)
Ravi Kumar
(Yahoo! Research)
Bradley Malin
(Vanderbilt University)
Frank McSherry
(Microsoft Research)
Yuval Nardi
(Technion - Israel Institute of Technology)
Kobbi Nissim
(Ben Gurion University of the Negev)
Sofya Raskhodnikova
(Pennsylvania State University)
Jerry Reiter
(Duke University)
Natalie Shlomo
(University of Southampton)
Vitaly Shmatikov
(University of Texas at Austin)
Chris Skinner
(University of Southampton)
Aleksandra Slavkovic
(Pennsylvania State University)
Adam Smith
(Pennsylvania State University)
Kunal Talwar
(Microsoft Research)
Salil Vadhan
(Harvard University)
Martin Wainwright
(University of California, Berkeley (UC Berkeley))
Larry Wasserman
(Carnegie-Mellon University)
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Application/Registration
An application/registration form is available at:
https://www.ipam.ucla.edu/elements/choose.aspx?pc=data2010
The application part is for people requesting financial support to attend
the workshop. If you don't intend to do this, you may simply register.
We urge you to apply as early as possible. Applications received by December 21, 2009 will receive fullest consideration.
Letters of reference may be sent to the address or email address below. Successful applicants will be notified as soon as funding decisions are made.
We have funding especially to support the attendance of recent PhD's, graduate
students, and researchers in the early stages of their career; however,
mathematicians and scientists at all levels who are interested in this area
are encouraged to apply for funding. Encouraging the careers of women and
minority mathematicians and scientists is an important component of IPAM's
mission and we welcome their applications.
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Contact Us:
Institute for Pure and Applied Mathematics (IPAM)
Attn: DATA2010
460 Portola Plaza
Los Angeles CA 90095-7121
Phone: 310 825-4755
Fax: 310 825-4756
Email: 
Website:
http://www.ipam.ucla.edu/programs/data2010/
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