Evaluating Performance Characteristics of Threshold Fully Homomorphic Encryption for Distributed Analytics Scenarios

Distributed analytics, such as federated learning, involve collaborative computation across multiple decentralized devices. This approach not only reduces data transfer costs but also offers some degree of protection for privacy-sensitive information. To achieve a higher level of privacy protection, it is recommended to use more advanced privacy-preserving technologies, such as homomorphic encryption. However, the use of holomorphic encryption schemes results in high computational costs. In this study, we evaluate the performance characteristics of threshold fully homomorphic encryption, a technique that can be effectively applied in multi-user environments and distributed analytics scenarios. We present results from the performance evaluation of the Cheon-Kim-Kim-Song scheme.