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Recent Advances in Wireless Body Area Networks (WBANs) offer unprecedented opportunities and challenges to the development of pervasive electronic healthcare (E-Healthcare) monitoring system. In E-Healthcare system, the processed data are patients' sensitive health data that are directly related to individuals' privacy. For this reason, privacy concern is of great importance for E-Healthcare system. Current existing systems for E-Healthcare services, however, have not yet provided sufficient privacy protection for patients. In order to offer adequate security and privacy, in this paper, we propose a privacy-enhanced scheme for patients' physical condition monitoring, which achieves dual effects: (1) providing unlinkability of health records and individual identity, and (2) supporting anonymous authentication and authorized data access. We also conduct a simulation experiment to evaluate the performance of the proposed scheme. The experimental results demonstrate that the proposed scheme achieves better performance in terms of computational complexity, communication overheads and querying efficiency compared with previous results.

With the development of the Internet, smart campus education and online social platforms have become the mainstream of establishing social relationships. Although many users communicate by social networks, the social networks are caught in the problem of relationship sparsity, which severely impedes users’ communication space. More fatally, in the course of building social relationships, the disclosure of sensitive information will cause users’ privacy vulnerable to be compromised by attackers. Therefore, this paper proposes a potential social relationships prediction approach based on locality-sensitive hashing (LSH) to address the above issues. Specifically, the LSH clusters similar users into the same bucket, and the fuzzy computing method is developed to predict the types of social relationships among these similar users. To further alleviate the relationship sparsity problem, the existing social network structure is utilized to predict users’ social relationships and relationship types. Furthermore, the rationality of prediction results is verified by using the social balance theory (SBT). Finally, massive experiments are executed on Epinions, and the experimental results further confirmed the efficiency and accuracy of our methodology in terms of link prediction while guaranteeing privacy-preservation.

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Cloud storage is one of the most widely-used storage services, because it can provide users with unlimited, scalable, low-cost and convenient resource services. When data is outsourced to cloud for storage, data security and access control are the two essential issues that need to be addressed. Attribute-based encryption (ABE) scheme can provide sufficient data security and fine-grained access control for cloud data. As more and more attention is drawn to privacy protection, privacy preservation becomes another urgent issue for cloud storage. In ABE, since the access policies are generally stored in clear text, it will lead to the disclosure of users’ privacy. Some works sacrifice computational efficiency, key length or ciphertext size for privacy concerns. To solve these problems, this paper proposes an efficient privacy-preserving attribute-based encryption scheme with hidden policy for outsourced data. Using the idea of Boolean equivalent transformation, the proposed scheme achieves fast encryption and privacy protection for both data owner and legitimate visitors. In addition, the proposed scheme can satisfy constant secret key length and reasonable size of ciphertext requirements. We also conduct theoretical security analysis, and carry out experiments to prove that the proposed scheme has good performance in terms of computation, communication and storage overheads.