Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving data integration and sharing
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
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 Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An Architecture for Privacy Preserving Collaborative Filtering on Web Portals
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
PinKDD'07: privacy, security, and trust in KDD post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data
Pattern Recognition Letters
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Emerging business models require organizations to collaborate with each other. This collaboration is usually in the form of distributed clustering to find optimal customer targets for effective marketing. This process is hampered by two problems (1) Inability of traditional clustering algorithm in finding local (subspace) patterns in distributed data and (2) Privacy policies of individual organizations limiting the process of information sharing. In this paper, we propose an efficient privacy preserving biclustering algorithm on horizontally partitioned data, referred to as Phoenix, which solves both of these problems. It assumes a malicious adversary model which is more practical than commonly employed semihonest adversary model. It is shown to outperform traditional K-means clustering algorithm in identifying local patterns. The distributed secure implementation of the algorithm is evaluated to be very efficient both in computation and communication requirements.