Data privacy protection in multi-party clustering

Privacy concerns over sensitive data have become important in knowledge discovery. Usually, data owners have different levels of concerns over different data attributes, which adds complexity to data privacy. Moreover, collusion among malicious adversaries poses a severe threat to data security. In this paper, we present an efficient clustering method for distributed multi-party data sets using the orthogonal transformation and perturbation techniques. Our method allows data owners to apply different levels of privacy to different attributes. The miner, while receiving the perturbed data, can still obtain accurate clustering results. This method protects data privacy, not only in the semi-honest situation, but also in the presence of collusion. The accuracy of the mining results and the privacy levels, and their relations to the parameters in the method are analyzed. Moreover, we propose an improved version of the method to alleviate the problem with a large number of participants. Experimental results demonstrate the effectiveness of our method as compared to existing methods.