A Fast and Privacy-Preserving Outsourced Approach for K-Means Clustering Based on Symmetric Homomorphic Encryption

Training a machine learning (ML) model always needs many computing resources, and cloud-based outsourced training is a good solution to address the issue of a computing resources shortage. However, the cloud may be untrustworthy, and it may pose a privacy threat to the training process. Currently, most work makes use of multi-party computation protocols and lattice-based homomorphic encryption algorithms to solve the privacy problem, but these tools are inefficient in communication or computation. Therefore, in this paper, we focus on the k-means and propose a fast and privacy-preserving method for outsourced clustering of k-means models based on symmetric homomorphic encryption (SHE), which is used to encrypt the clustering dataset and model parameters in our scheme. We design an interactive protocol and use various tools to optimize the protocol time overheads. We perform security analysis and detailed evaluation on the performance of our scheme, and the experimental results show that our scheme has better prediction accuracy, as well as lower computation and total overheads.