An adaptive access control scheme based on trust degrees for edge computing

Abstract

Access control is an essential mechanism in collaborative environments, which guarantees the security of shared resources. With the intertwinement of emerging technologies likes edge computing, cloud, Artificial Intelligence (AI), and Internet of Things (IoT), access control will encounter its innovation and development shortly. Whereas, traditional access control did not consider the adaptive and fine-grained requirements in edge computing. In addition, the integrity of the accessed data is not considered in the traditional access control scheme. In this paper, inspired by the technologies in AI and cloud, an adaptive access control scheme based on trust degrees is proposed. The trust degrees of users and the quality of data are defined. On this basis, the trust degrees-based adaptive access control framework is proposed. Moreover, in the proposed scheme, technologies in AI and cryptography are converged. In particular, the ciphertext policy attribute-based encryption (CP-ABE) is integrated with the classification and recommendation algorithms in AI, thereby providing adaptive and fine-grained properties for access control in edge computing. Moreover, the integrity of the accessed data is guaranteed by the auditing technique in cryptography. Finally, the proposed access control scheme is implemented by using the PBC library. For supporting the efficient computation in edge computing, all types of elliptic curves provided by the PBC library are tested. The experimental result shows that Type D curve is the most efficient in implementing the proposed scheme.