As we acknowledge that vehicle-to-grid (V2G) network holds obvious predominance in economic benefits, it attracts wide attentions of power system and electric vehicle (EV) users. However, lots of security issues hinder its further development. For example, many existing V2G schemes use the bilinear pairing to implement authentication protocol, resulting in high computational costs. In addition, the system master key in the existing V2G scheme is generated independently by a trusted third party, so the system master key is vulnerable to leakage if the third party is compromised by the adversary. In this article, we propose a novel privacy-preserving authentication scheme for V2G networks. Specifically, this scheme employs the nonsupersingular elliptic curve to construct a lightweight authentication protocol for EV and smart grid. Meanwhile, system master key in the proposed scheme is generated by the key agreement protocol, i.e., a protocol that is constructed using a secure two-party computation between the third party authority and the dispatching center. Security analysis shows that the proposed scheme is provably secure under the random oracle model. Compared with related schemes, the proposed scheme can not only improve the authentication efficiency of V2G networks, but also prevent internal attacker. Experimental results indicate that our scheme is of practical significance for V2G networks.

The development of Big Data and cloud computing has brought great progress to medical diagnosis and clinical services. However, as disease diagnosis technologies usually use lots of clinical medical data, patients’ privacy becomes increasingly important since the clinical results of medical data are much sensitive. In this article, to deal with the privacy preservation issue of patient’s medical data, we propose a privacy-preserving disease diagnosis scheme that is based on the Mahalanobis distance test, in which the role of the system consisting of query user (QU), aided cloud server (ACS), and classification cloud server (CCS) is to jointly calculate and protect the diagnosis data over the sensitive and outsourced medical data. In the diagnosis model, we outsource the clinical diagnosis process to be dealt with by the CCS, and then, the ACS can reduce the computational cost of the user side. We utilize the homomorphic re-encryption scheme to realize a secure computation over the outsourced medical data, and then, employ a secure multiplication (SM) protocol to implement the privacy-preserving Mahalanobis distance to output the disease diagnosis. Concretely, we provide an extended SM algorithm to solve the problem of multiplication of two encrypted data, and a minimum value comparison algorithm over ciphertext for comparing the encrypted Mahalanobis distance. Finally, we give the experimental performance in real data sheets, and the experimental results indicate that our scheme provides a lower computational cost in practical diagnosis services.

Vehicle-to-grid (V2G) network is an emerging network environment that can improve grid efficiency and alleviate fluctuations caused by renewable energy access to the grid. However, security and efficiency of V2G network is a very challenging and important factor in the case of large-scale electric vehicles (EVs) access to the grid. In this paper, we propose a secure and membership-based data sharing scheme in V2G networks. This scheme employs the online/offline ciphertext policy attribute-based encryption mechanism combined with fog computing, thus preserving data privacy and saving system resources. Concretely, the heavy computational operations of encryption and decryption are performed by the fog devices in offline encryption phase and decryption phase, respectively. What’s more, the service information is divided into multiple parts in term of different membership levels and is encrypted by the control center (CC) through different access policy. The ciphertext can only be decrypted when the EV has a set of attributes that satisfy the corresponding membership level of access policy. Security and performance analyses demonstrate that the proposed scheme not only protects data privacy, but also saves the computational costs of CC and EV. Therefore, our scheme is more suitable for the deployment and adoption of V2G networks.