Electronic health record exchanges are crucial functions of modern healthcare systems. These components are fundamental in providing quality care and enable for a larger spectrum of services. A framework which protects patient information during data exchanges is essential for healthcare systems. To achieve security and privacy‐preservation for information exchange, we propose a consent‐based access control (CBAC) mechanism for healthcare systems. A consent is an authorization initiated by a patient for an intended data requester via an agreement between them. After obtaining the consent from the patient, a healthcare organization can gain access to the data, which is encrypted by a healthcare provider. This is achieved by a cryptographic primitive: conditional proxy re‐encryption. By doing so, patient medical data is protected against access of unauthorized parties, including public data center. Additionally, the proposed scheme achieves collusion resistance. Furthermore, mutual authentication and contextual privacy are attained. Performance evaluation demonstrates that the proposed CBAC scheme can achieve security and privacy preservation with high computational efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

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Recently, concolic execution has become a hotspot in the domain of software testing and program analysis. However, a practical challenge, called path divergence, impairs the soundness and completeness of concolic execution. A path divergence indicates the tested program runs an unpredicted path. In this work, we carry out a comprehensive empirical study on path divergences using an open-source concolic execution tool, named CREST. To make the investigation representative, we select 120 test units randomly from 21 different open-source programs. The results are interesting, and will provide insight to solve the challenging path-divergence problem. First, about one-half of test units suffer from path divergences, indicating path divergences are so prevalent that the issue is worthy of great attention. Second, quite a number of generated test inputs drive test units to take divergent paths. This means testers need considerable effort to eliminate the misleading test inputs before aggregating them to a test suite. Third, we dig out ten divergent patterns through manual analysis of each path divergence. Among them, the three most prevalent ones, which are exceptions, external calls, and type casts, lead to almost 82% of path divergences. Finally, we discuss several countermeasures to overcome path divergences.