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演讲人：Ting Yu 
Associate professor Department of Computer Science North Carolina State University 时间：5 月 22 日（周三）15:00地点：软件楼 102 第二会议室联系人：杨珉（m_yang@fudan.edu.cn）Abstract:Procedures to anonymize data sets are vital for companies, government
agencies and other bodies to meet their obligations to share data without
compromising the privacy of the individuals contributing to it. Initial efforts
for developing privacy models focused on weakening the connection
between quasi-identifiers and sensitive values. These models are now
considered to offer insufficient privacy. Differential privacy has recently
emerged as the de facto standard for private data release. This makes
it possible to provide strong theoretical guarantees on the privacy and
utility of released data.
In this talk we apply differential privacy to the release of spatial data, i.e.,
any multidimensional data that can be indexed by a tree structure, by
adapting standard spatial indexing methods such as quad-trees and
kd-trees. Various basic steps, such as choosing splitting points and
describing the distribution of points within a region, must be done privately,
and the guarantees of the different building blocks must be composed into
an overall guarantee. We show that microdata anonymized by applying
private spatial decompositions preserves useful aggregate information
such that range count queries can still be answered with high accuracy.Bio: Ting Yu is an Associate Professor in the Department of Computer Science,
North Carolina State University. He obtained his PhD from the University
of Illinois at Urbana-Champaign in 2003, MS from the University of
Minnesota in1998, and BS from Peking University in 1997, all in computer
science. His research is in security, with a focus on data security and privacy,
trust management and security policies. He is a recipient of the NSF
CAREER Award.讲座照片：
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