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

Wenying Zheng, Chin Feng Lai, Debiao He, Neeraj Kumar, Bing Chen

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號9082138
頁(從 - 到)4238-4247
頁數10
期刊IEEE Transactions on Industrial Informatics
17
發行號6
DOIs
出版狀態Published - 2021 六月

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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