Communications of the ACM
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Practical Threshold RSA Signatures without a Trusted Dealer
EUROCRYPT '01 Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology
PKC '01 Proceedings of the 4th International Workshop on Practice and Theory in Public Key Cryptography: Public Key Cryptography
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Semi-supervised protein classification using cluster kernels
Bioinformatics
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
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
Link analysis for private weighted graphs
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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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We propose a novel privacy preserving learning algorithm that achieves semi-supervised learning in graphs. In real world networks, such as disease infection over individuals, links (contact) and labels (infection) are often highly sensitive information. Although traditional semisupervised learning methods play an important role in network data analysis, they fail to protect such sensitive information. Our solutions enable to predict labels of partially labeled graphs without disclosure of labels and links, by incorporating cryptographic techniques into the label propagation algorithm. Even when labels included in the graph are kept private, the accuracy of our PPLP is equivalent to that of label propagation which is allowed to observe all labels in the graph. Empirical analysis showed that our solution is scalable compared with existing privacy preserving methods. The results with human contact networks showed that our protocol takes only about 10 seconds for computation and no sensitive information is disclosed through the protocol execution.