Efficient Two-Party Privacy Preserving Collaborative k-means Clustering Protocol Supporting both Storage and Computation Outsourcing

Privacy preserving collaborative data mining aims to extract useful knowledge from distributed databases owned by multiple parties while keeping the privacy of both data and mining result. Nowadays, more and more companies reply on cloud to store data and handle with data. In this context, privacy preserving collaborative k-means clustering framework was proposed to support both storage and computation outsourcing for two parties. However, the computing cost and communication overhead are too high to practical. In this paper, we propose to encrypt each party’s data once and then store them in cloud. Privacy preserving k-means collaborative clustering protocol is executed mainly at cloud side, with total \(O(k(m+n))\)-round interactions among the two parties and the cloud. Here, m and n means that the total numbers of records for the two parties, respectively. The protocol is secure in the semi-honest security model and especially secure in the malicious model supporting only one party corrupted during k centroids re-computation. We also implement it in real cloud environment using e-health data as the testing data.