, Volume 11, Issue 2, pp 141-159

Privacy Preserving Link Analysis on Dynamic Weighted Graph

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Link analysis algorithms have been used successfully on hyperlinked data to identify authoritative documents and retrieve other information. They also showed great potential in many new areas such as counterterrorism and surveillance. Emergence of new applications and changes in existing ones created new opportunities, as well as difficulties, for them: (1) In many situations where link analysis is applicable, there may not be an explicit hyperlinked structure. (2) The system can be highly dynamic, resulting in constant update to the graph. It is often too expensive to rerun the algorithm for each update. (3) The application often relies heavily on client-side logging and the information encoded in the graph can be very personal and sensitive. In this case privacy becomes a major concern. Existing link analysis algorithms, and their traditional implementations, are not adequate in face of these new challenges. In this paper we propose the use of a weighted graph to define and/or augment a link structure. We present a generalized HITS algorithm that is suitable for running in a dynamic environment. The algorithm uses the idea of “lazy update” to amortize cost across multiple updates while still providing accurate ranking to users in the mean time. We prove the convergence of the new algorithm and evaluate its benefit using the Enron email dataset. Finally we devise a distributed implementation of the algorithm that preserves user privacy thus making it socially acceptable in real-world applications.

This material is based upon work supported by the National Science Foundation under Grant No. 0222745.
Part of this work was presented at the SDM05 Workshop on Link Analysis in Newport Beach, California, April 2005.
Yitao Duan is a Ph.D. candidate in Computer Science at the University of California, Berkeley. His research interests include practical privacy enhancing technologies for a variety of situations including: ubiquitous computing, collaborative work, smart spaces, and location-aware services etc. His research goal is to develop provably strong (cryptographic and information-theoretic) protocols that are practically realizable. He received his B.S. and M.S. in Mechanical Engineering from Beijing University of Aeronautics and Astronautics, China in 1994 and 1997.
Jingtao Wang is a Ph.D. student in Computer Science at the University of California, Berkeley. His research interests include context-aware computing, novel end-user interaction techniques and statistical machine learning. He was a research member, later a staff research member and team lead at IBM China Research Lab from 1999 to 2002, working on online handwriting recognition technologies for Asian languages. He received his B.E. and M.E. in electrical and computer engineering from Xi'an Jiaotong University, China in 1996 and 1999. He is a member of the ACM and ACM SIGCHI since 2000.
Matthew Kam is a Ph.D. student in computer science at the University of California, Berkeley working on educational technology and human-computer interaction for low-income communities in developing regions. He received a B.A. in economics and a B.S. in Electrical Engineering and Computer Sciences, also from Berkeley. He is a member of the ACM and Engineers for a Sustainable World.
John Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering in Computer Science at the University of California, Berkeley. His research is in human-computer interaction, with an emphasis on modeling methods and privacy approaches using cryptography. He received his Ph.D. in 1987 at the MIT AI Lab. His dissertation on Robot Motion Planning received the ACM dissertation award. He received a Packard Foundation Faculty Fellowship and a Presidential Young Investigator Award. His peer-reviewed publications span robotics, computational geometry, physical simulation, computational algebra, theory and algorithms, information retrieval, HCI and CSCW and cryptography.