| Summary: | Edge computing provides users with nearby services, promotes the development of the Internet of Things and transforms the mode of traditional electronic medical system that improves the speed and accuracy of medical diagnosis. However, edge computing has introduced new security challenges, such as the difficulty in applying standard encryption algorithms because of the limited computing capability of devices on the edge and concerns of privacy leakage. In this article, we propose a data access scheme by employing a lightweight attribute-based encryption (ABE) to address the security weaknesses of edge computing for e-health. Our novel modular exponential outsourcing algorithm outsources the shared data to the untrusted edge server and verifies the correctness of the returned results while achieving a lightweight computational load. The proposed Mask Algorithm along with blind pairs conceals the bases and exponents of the encrypted data, thus preserving the privacy of sensitive data in the process of outsourced computing. Security analysis and performance evaluation confirmed the security and efficiency of the proposed method.
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