Privacy-Aware Efficient Fine-Grained Data Access Control in Internet of Medical Things Based Fog Computing

Xiaofan Wang*, Lei Wang, Yujun Li, Keke Gai

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

49 引用 (Scopus)

摘要

The recent development of cloud computing has empowered the Internet-based services, which enable users to gain a broad scope of access to their applications, such as Internet of Medical Things (IoMT). Considering the efficiency performance, the privacy protection is often kept at a lower level in order to ensure the application can offer a higher level performance. However, this mechanism also causes a serious concern of privacy hazards due to the information over collections operated by apps/applications. Addressing this privacy issue, this paper proposes an approach that is designed to provide high-level privacy protection without lowering down the efficiency in cloud/fog computing (especially in biological systems of IoMT). The proposed model is called fog-based access control model. The fine-grained data access control is combined with the implementation of fog computing in the proposed approach. Our simulation experiment has shown that our approach could offer high-level privacy protection within a shortened execution time, thus it can be useful for IoMT-based applications.

源语言英语
文章编号8412491
页(从-至)47657-47665
页数9
期刊IEEE Access
6
DOI
出版状态已出版 - 17 7月 2018

指纹

探究 'Privacy-Aware Efficient Fine-Grained Data Access Control in Internet of Medical Things Based Fog Computing' 的科研主题。它们共同构成独一无二的指纹。

引用此