With the growth of public demand for online access to health services, many efforts have been devoted to personal health records (PHR) in cloud computing. It enables patients to manage their personal health information (PHI) in cloud servers, which greatly facilitates the collection, access and sharing of PHI. Since cloud servers are not fully trusted, it is desirable that the PHI can be encrypted for privacy protection before uploaded to the cloud. Besides the privacy of PHI, fine-grained and flexible search control is also strongly desired for a secure PHR system. In this article, we first present attribute set based keyword search (ASBKS) which can realize fine-grained keyword search of encrypted PHR. Compared with the existing searchable encryption with access control, the proposed ASBKS can achieve more flexibility in user attributes organization and more efficiency in specifying policies. Furthermore, we present a hierarchical ASBKS scheme to improve scalability by extending ASBKS with a hierarchical structure of users. We implement our ASBKS scheme and the experimental results demonstrate that it is both efficient and flexible for encrypted PHR in cloud computing.

Recently, more and more data have been stored in the cloud with keyword indices so that the data users can make search over the databases. In some of these database applications, the query frequency analysis of keywords is quite important to the optimization of the databases and it is easy to be implemented when the data are not encrypted. However, with the growing demands of data privacy, it is desired that the data should be encrypted before uploaded to the cloud. Searchable encryption has been proposed which enables users to make keyword search over the encrypted data with keyword privacy. In a secure searchable encryption scheme, it is required that the keyword in each query should not be revealed. So it becomes challenging to analyze the query frequency of keywords in an encrypted database which has the pressing need of optimization. In this paper, we first consider this problem and present an efficient solution to it which enables the query frequency analysis of keywords without destroying the privacy of the encrypted data and the identity privacy of data users. We also simulate our solution and show that it is practical to the real applications.