Secured Fine-Grained Selective Access to Outsourced Cloud Data in IoT Environments

Authors:
Publisher website
Google Scholar
Abstract
With the vast increase in data transmission due to a large number of information collected by devices, data management and security has been a challenge for organisations. Many data owners outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, data owners, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorised access to data but prove to be deficient in providing suitable solutions to the problem. In this paper, we proffer a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an Attribute-based Encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy(confidentiality) of outsourced data. Our work considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a data owner. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.
Funding Information
  • National Key Research and Development Project (2017YFB0802900)
  • Program of International Science and Technology Cooperation and Exchange of Sichuan Province (2017HH0028, 2018HH0102, 2019YFH0014)
  • Sichuan Science and Technology Program (2017CC0071)
  • National Natural Science Foundation of China (61572115)
  • CCF-Tencent Open Research Fund