Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A decentralized algorithm for spectral analysis
Journal of Computer and System Sciences
Secure two-party k-means clustering
Proceedings of the 14th ACM conference on Computer and communications security
Link analysis for private weighted graphs
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A Framework for Computing the Privacy Scores of Users in Online Social Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
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We consider the clustering problem in a private social network, in which all vertices are independent and private, and each of them knows nothing about vertices other than itself and its neighbors. Many clustering methods for networks have recently been proposed. Some of these works have dealt with a mixed network of assortative and disassortative models. These methods have been based on the fact that the entire structure of the network is observable. However, entities in real social network may be private and thus cannot be observed. We propose a privacy-preserving EM algorithm for clustering on distributed networks that not only deals with the mixture of assortative and disassortative models but also protects the privacy of each vertex in the network. In our solution, each vertex is treated as an independent private party, and the problem becomes an n -party privacy-preserving clustering, where n is the number of vertices in the network. Our algorithm does not reveal any intermediate information through its execution. The total running time is only related to the number of clusters and the maximum degree of the network but this is nearly independent of the total vertex number.