No Free Lunch in Data Privacy
When: Wednesday, March 7, 2012 - 9:45am - 11:00am
Where: KEC 1007
Speaker Information
Speaker Name: Ashwin Machanavajjhala
Speaker Title/Description:
Senior Research Scientist
Yahoo! Research
Speaker Biography:
Ashwin Machanavajjhala is a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in data privacy with a specific focus on formally reasoning about privacy under probabilistic adversary models. He is also interested in big-data management and statistical methods for information integration. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University. His thesis work on defining and enforcing privacy was awarded the 2008 ACM SIGMOD Jim Gray Dissertation Award Honorable Mention. He has also received an M.S. from Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.
Abstract:
Tremendous amounts of personal data about individuals are being collected and
shared online. Legal requirements and an increase in public awareness due to
egregious breaches of individual privacy have made data privacy an important
field of research. Recent research, culminating in the development of a
powerful notion called differential privacy, have transformed this field from a
black art into a rigorous mathematical discipline.
This talk critically analyzes the trade-off between accuracy and privacy in the
context of social advertising -- recommending people, products or services to
users based on their social neighborhood. I will present a theoretical upper
bound on the accuracy of performing recommendations that are solely based on a
user's social network, for a given level of (differential) privacy of sensitive
links in the social graph. I will show using real networks that good private
social recommendations are feasible only for a small subset of the users in the
social network or for a lenient setting of privacy parameters.
I will also describe some exciting new research about a no free lunch theorem,
which argues that privacy tools (including differential privacy) cannot
simultaneously guarantee utility as well as privacy for all types of data, and
conclude with directions for future research in data privacy and big-data
management.
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