Private, Accurate and Communication Efficient Clustering Over Vertically Distributed Dataset

Clustering is a crucial unsupervised machine learning algorithm extensively used in various practical applications, such as patient refinement and fraud detection, which often involve vertically distributed data across multiple data centers. However, sharing datasets directly is typically prohibited under GDPR due to potential privacy breaches. Therefore, privacy-preserving joint clustering for vertically distributed datasets is highly desired. In this paper, we propose Privacy-Preserving Vertically Federated Clustering (PPVFC), a solution that not only achieves this goal but also significantly reduces computational and communication overhead for each data owner (DO). Unlike most previous works that achieve the goal with a single privacy-enhancing technology, PPVFC jointly leverages multiparty homomorphic encryption (MHE) and multiparty computation (MPC) to efficiently interleave communication-lightweight homomorphic computations on the local dataset with operations over collectively secret-shared intermediate data. Specifically, we design a coefficient-wise encoding for MHE to pack large datasets and minimize communication costs. Additionally, we develop a round-efficient bit extraction protocol for determining the minimum distance. Through extensive experiments and security analysis, we demonstrate the practical performance and robust security guarantees of PPVFC.