Efficient and Privacy-Preserving k-Means Clustering for Big Data Mining

Recent advances in sensing and storing technologies have led to big data age where a huge amount of data are distributed across sites to be stored and analysed. Indeed, cluster analysis is one of the data mining tasks that aims to discover patterns and knowledge through different algorithmic techniques such as k-means. Nevertheless, running k-means over distributed big data stores has given rise to serious privacy issues. Accordingly, many proposed works attempted to tackle this concern using cryptographic protocols. However, these cryptographic solutions introduced performance degradation issues in analysis tasks which does not meet big data properties. In this work, we propose a novel privacy-preserving k-means algorithm based on a simple yet secure and efficient multiparty additive scheme that is cryptography-free. We designed our solution for horizontally partitioned data. Moreover, we demonstrate that our scheme resists against adversaries passive model.

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