Multi-User Encrypted Machine Learning Based on Partially Homomorphic Encryption

Machine-learning applications are becoming increasingly widespread. However, machine learning is highly dependent on high-quality, large-scale training data. Due to the limitations of data privacy and security, in order to accept more user data, users are required to participate in the computation themselves through the secure use of secret keys. In this paper, we propose a multi-user encrypted machine-learning system based on partially homomorphic encryption, which can be realized for the purpose of supporting encrypted machine learning under multiple users. In this system, offline homomorphic computation is provided, so that users can support homomorphic computation without interacting with the cloud after locally executing encryption, and all computational parameters are computed in the initial and encryption phases. In this system, the isolation forest algorithm is modified appropriately so that its computation can be within the supported homomorphic computation methods. The comparison with other schemes in the comparison experiments reflects this scheme’s computational and communication advantages. In the application experiments, where anomaly detection is taken as the goal, the encrypted machine-learning system can provide more than 90% recall, illustrating this scheme’s usability.