Privacy-preserving k-means clustering with local synchronization in peer-to-peer networks

Abstract

k-means clustering, which partitions data records into different clusters such that the records in the same cluster are close to each other, has many important applications such as image segmentation and genes detection. While the k-means clustering has been well-studied by a significant amount of works, most of the existing schemes are not designed for peer-to-peer (P2P) networks. P2P networks impose several efficiency and security challenges for performing clustering over distributed data. In this paper, we propose a novel privacy-preserving k-means clustering scheme over distributed data in P2P networks, which achieves local synchronization and privacy protection. Specifically, we design a secure aggregation protocol and a secure division protocol based on homomorphic encryption to securely compute clusters without revealing the privacy of individual peer. Moreover, we propose a novel massage encoding method to improve the performance of our aggregation protocol. We formally prove that the proposed scheme is secure under the semi-honest model and demonstrate the performance of our proposed scheme.