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

Zakaria Gheid (1) , Yacine Challal (1, 2)

1 LMCS - Laboratoire de Méthodes de Conception de Systèmes
2 CERIST - Centre de recherche sur l'Information Scientifique et Technique

—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 multi-party 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.

Mots clés: horizontally partitioned data; k-means clustering; privacy; efficiency

Domaines: Réseaux et télécommunications [cs.NI]

Zakaria Gheid, Yacine Challal. Efficient and Privacy-Preserving k-Means Clustering for Big Data Mining. IEEE TristCom, Aug 2016, Tianjin, China. pp.791 - 798, ⟨10.1109/TrustCom.2016.0140⟩. ⟨hal-01466904⟩