Secure and Evaluable Clustering Based on a Multifunctional and Privacy-Preserving Outsourcing Computation Toolkit

Although tremendous revolution has been made in the emerging cloud computing technologies over digital devices, privacy gradually becomes a big concern in outsourcing computation. Homomorphic encryption has been proposed to facilitate the preservation of data privacy while computational tasks being executed on ciphertext. However, many existing studies only support limited homomorphic calculation functions which barely satisfy complex computing tasks such as machine learning with massive computing resources and rich types of function. To address this problem, a novel multifunctional and privacy-preserving outsourcing computation toolkit is proposed in this paper, which supports several homomorphic computing protocols including division and power on ciphertext of integers and floating point numbers. Specifically, we first implement the homomorphic mutual conversion protocol between integer and floating point ciphertext to balance the efficiency and feasibility, considering the high-precision ciphertext operation on floating point numbers costs 100x computational overhead than that on integers. Second, we implement a homomorphic K-means algorithm based on our proposed toolkit for clustering and design the homomorphic silhouette coefficient as the evaluation index, thereby providing an informative cluster assessment for local users with limited resources. Then, we simulate the protocols of our proposed toolkit to explore the parameter sensitivity in terms of computational efficiency. Last, we report security analysis to prove the security of our toolkit without privacy leakage to unauthorized parties. Comprehensive experiments further demonstrate the efficiency and utility of our toolkit.