A Privacy-Preserving Decision Tree Evaluation Scheme for Multiple Wearable Devices

Privacy-Preserving Decision Tree (PPDT) evaluation is a typical algorithm in Machine Learning as a Service scenario. For services like health analysis, PPDT evaluation by fusing private data from multiple Wearable Devices is necessary. Homomorphic Encryption (HE) implements PPDT evaluation without revealing locally sensitive data. However, existing HE schemes need better scalability and are incompatible with clients such as Wearable Devices, which have limited computing power, bandwidth, memory, and storage. This paper proposed a non-interactive PPDT evaluation scheme for multiple Wearable Devices. We apply transciphering technology and an efficient Fully Homomorphic Encryption (FHE) cryptosystem to optimize overall performance. Simultaneously, a Computing Server and a Trusted Third Party (TTP) are adopted to perform all the homomorphic operations on the server side, leaving clients to encrypt and decrypt local data using stream ciphers. Then, we implement an instance of our scheme based on FiLIP stream cipher, a homomorphic comparison algorithm, and a homomorphic traverse algorithm. The experimental results show that the various overheads on the client side are relatively lower.