Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data

The challenge in privacy-preserving data mining is avoiding the invasion of personal data privacy. Secure computation provides a solution to this problem. With the development of this technique, fully homomorphic encryption has been realized after decades of research; this encryption enables the computing and obtaining results via encrypted data without accessing any plaintext or private key information. In this paper, we propose a privacy-preserving clustering using representatives (CURE) algorithm over arbitrarily partitioned data using fully homomorphic encryption. Our privacy-preserving CURE algorithm allows cooperative computation without revealing users' individual data. The method used in our algorithm enables the data to be arbitrarily distributed among different parties and to receive accurate clustering result simultaneously.