Communications of the ACM
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Journal of the ACM (JACM)
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy Preserving Link Analysis on Dynamic Weighted Graph
Computational & Mathematical Organization Theory
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
A decentralized algorithm for spectral analysis
Journal of Computer and System Sciences
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy-preserving reinforcement learning
Proceedings of the 25th international conference on Machine learning
Privacy preserving semi-supervised learning for labeled graphs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Privacy-Preserving EM algorithm for clustering on social network
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Privacy preservation of user history graph
WISTP'12 Proceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems
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Link analysis methods have been used successfully for knowledge discovery from the link structure of mutually linking entities. Existing link analysis methods have been inherently designed based on the fact that the entire link structure of the target graph is observable such as public web documents; however, link information in graphs in the real world, such as human relationship or economic activities, is rarely open to public. If link analysis can be performed using graphs with private links in a privacy-preserving way, it enables us to rank entities connected with private ties, such as people, organizations, or business transactions. In this paper, we present a secure link analysis for graphs with private links by means of cryptographic protocols. Our solutions are designed as privacy-preserving expansions of well-known link analysis methods, PageRank and HITS. The outcomes of our protocols are completely equivalent to those of PageRank and HITS. Furthermore, our protocols theoretically guarantee that the private link information possessed by each node is not revealed to other nodes. %We demonstrate the efficiency of our solution by experimental studies, comparing with existing solutions, such as secure function evaluation, decentralized spectral analysis, and privacy-preserving link-analysis.