Privacy Preserving Clustering for Multi-party

Privacy concerns on sensitive data are now becoming indispensable in data mining and knowledge discovering. Data owners usually have different concerns for different data attributes. Meanwhile the collusion among malicious adversaries produces a severe threat to the security of data. In this paper, we present an efficient method to generate the attribute-wised orthogonal matrix for data transformation. Moreover, we introduce a privacy preserving method for clustering problem in multi-party condition. Our method can not only protect data in the semi-honest model but also in the malicious one. We also analyze the accuracy of the results, the privacy levels obtained, and their relations with the parameters in our method.

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