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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number9082138
Pages (from-to)4238-4247
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number6
DOIs
Publication statusPublished - 2021 Jun

All Science Journal Classification (ASJC) codes

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

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