Privacy-Preserving Two-Party k-Means Clustering in Malicious Model

Abstract

In data mining, clustering is a well-known and useful technique. One of the most powerful and frequently used techniques is k-means clustering. Most of the privacy-preserving solutions based on cryptography proposed by different researchers in recent years are in semi-honest model, where participating parties always follow the protocol. This model is realistic in many cases. But providing stonger solutions considering malicious model would be more useful for many practical applications because it tries to protect a protocol from arbitrary malicious behavior using cryptographic tools. In this paper, we have proposed a new protocol for privacy-preserving two-party k-means clustering in malicious model. We have used threshold homomorphic encryption and non-interactive zero knowledge protocols to construct our protocol according to real/ideal world paradigm.