An efficient approach for privacy preserving distributed K-means clustering based on Shamir's secret sharing scheme

Abstract

Privacy preserving data mining has gained considerable attention because of the increased concerns to ensure privacy of sensitive information. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the overheads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed K-Means clustering using Shamir's secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and does not require a trusted third party. In addition, it has negligible computational overhead as compared to the existing approaches. © 2012 IFIP International Federation for Information Processing.