Machine learning classification is an useful tool for trend prediction by analyzing big data. As supporting homomorphic operations over encrypted data without decryption, fully homomorphic encryption (FHE) contributes to machine learning classification without leaking user privacy, especially in the outsouring scenario. In this paper, we propose an improved FHE scheme based on HElib, which is a FHE library implemented based on Brakerski's FHE scheme. Our improvement focuses on two aspects. On the one hand, we first use the relinearization technique to reduce the ciphertext size, and then the modulus switching technique is used to reduce the modulus and decryption noise. On the other hand, we need no relinearization and modulus switching if there is additive homomorphic or no homomorphic operation in the multiplicative ciphertext's next homomorphic operation. Homomorphic comparison protocol, private hyperplane decision-based classification and private Naïve Bayes classification are implemented by additive homomorphic and multiplicative homomorphic firstly. In our homomorphic comparison protocol, the number of interactions is reduced from 3 to 1. We choose the proposed FHE scheme to implement private decision tree classification. Simulation results show that the efficiency of our FHE scheme and implementation of private decision tree classification are more efficient than other two schemes.

In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health data. In this paper, based on Cheon et al.’s approximate homomorphic encryption scheme, we first propose secure computation protocols for implementing comparison, maximum, exponentiation, and division. Next, we design a homomorphic reciprocal of square root protocol firstly, which only needs one approximate computation. Based on the proposed secure computation protocols, we design a secure asynchronous advantage actor‐critic reinforcement learning algorithm for the first time. Then, it is used to implement a secure treatment decision‐making algorithm. Simulation results show that our secure computation protocols and algorithms are feasible.