Privacy Preserving Link Analysis on Dynamic Weighted Graph
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
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.
- Brin, S. and L. Page (1998), “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, in 7th World Wide Web Conference, Brisbane, Australia.
- Canny, J. (2002), “Collaborative Filtering with Privacy”, in IEEE Symposium on Security and Privacy, Oakland, CA, U.S.A, pp. 45–57
- Canny, J. and S. Sorkin (2004), “Practical Large-Scale Distributed Key Generation”, Eurocrypt 2004.
- Carriere, J. and R. Kazman (1997), “WebQuery: Searching and Visualizing the Web through Connectivity”, in Proceedings of the International WWW Conference.
- Chakrabarti, S., B. Dom, D. Gibson, J. Kleinberg, P. Raghavan, and S. Rajagopalan (1998), “Automatic Resource List Compilation by Analyzing Hyperlink Structure and Associated Text”, in Proceedings of the 7th International World Wide Web Conference.
- Cohen, W.W. (2005), Enron Email Dataset, http://www-2.cs.cmu.edu/~enron/.
- Corrada-Emmanuel, A. (2005), Enron Email Dataset Research, http://ciir.cs.umass.edu/~corrada/enron/.
- Fouque, P. and J. Stern (2001), “One Round Threshold Discrete-Log Key Generation without Private Channels”, Public Key Cryptography, pp. 300–316.
- Gennaro, R., S. Jarecki, H. Krawczyk, and T. Rabin (1999), “Secure Distributed Key Generation for Discrete-Log Based Cryptosystems”, Lecture Notes in Computer Science, 1592, 295–310.
- Golub, G.H. and C.F. Van Loan (1989), Matrix Computations. Johns Hopkins University Press.
- Kautz, H., B. Selman, and A. Milewski (1996), “Agent Amplified Communication”, AAAI-96, Portland, Oregon, MIT Press, Cambridge, MA, 3–9.
- Kautz, H., Selman, B., Shah, M. (1997) Combining Social Networks and Collaborative Filtering. Communications of ACM 40: pp. 63-65 CrossRef
- Kleinberg, J.M. (1999) Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46: pp. 604-632 CrossRef
- MacDonald, D.W. and M.S. Ackerman (1998), “Just Talk to Me: A Field Study of Expertise Location”, in ACM CSCW-98, pp. 315—324.
- Newell, A. and P.S. Rosenbloom (1981), “Mechanisms of Skill Acquisition and the Law of Practice”, in J.R. Anderson (Ed.), Cognitive Skills and their Acquisition, Hillsdale, NJ: Earlbaum, pp. 1–55.
- Ng, A.Y., A.X. Zheng, and M.I. Jordan (2001a), “Link Analysis, Eigenvectors and Stability”, in Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, Washington, United States, pp. 903–910.
- Ng, A.Y., A.X. Zheng, and M.I. Jordan (2001b), “Stable Algorithms for Link Analysis”, in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, United States, pp. 258–266.
- Pedersen, T. (1991), “A Threshold Cryptosystem without a Trusted Party”, in Proceedings of EUROCRYPT ’91, Springer-Verlag LNCS, vol. 547, pp. 522–526.
- Pirolli, P., J. Pitkow, and R. Rao (1996), “Silk from a Sow's Ear: Extracting Usable Structures from the Web”, in Proceedings of ACM Conference on Human Factors in Computing Systems, ACM Press.
- Polak, E. (1971), Computational Methods in Optimization. Academic Press.
- Schwartz, M.F., Wood, D.C.M. (1993) Discovering Shared Interests Using Graph Analysis. Communications of ACM 36: pp. 78-89 CrossRef
- Salton, G. (1989), Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley.
- Stewart, G.W. and J. Sun (1990), Matrix Perturbation Theory. Academic Press.
- Strang, G. (1980), Linear Algebra and Its Applications, 2nd edition. Academic Press.
- Privacy Preserving Link Analysis on Dynamic Weighted Graph
Computational & Mathematical Organization Theory
Volume 11, Issue 2 , pp 141-159
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- link analysis
- data mining
- text analysis
- graph algorithms
- lazy update