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

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 (...

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Bibliographic Details
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
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Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2991204