Secure Storage Auditing With Efficient Key Updates for Cognitive Industrial IoT Environment

• Published in: IEEE transactions on industrial informatics 2021-06, Vol.17 (6), p.4238-4247
• Main Authors: Zheng, Wenying, Lai, Chin-Feng, He, Debiao, Kumar, Neeraj, Chen, Bing
• Format: Article
• Language: English
• Subjects: Auditing; Big Data; Cloud computing; cognitive computing; Data storage; Electronic devices; Industrial applications; industrial IoT; Industries; Internet of Things; Memory; Production; Security; Servers
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.2991204

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.