Privacy-Aware Efficient Fine-Grained Data Access Control in Internet of Medical Things Based Fog Computing
Author(s) -
Xiaofan Wang,
Lei Wang,
Yujun Li,
Keke Gai
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2856896
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.
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