Title: Data protection and provenance in cloud of things environment: research challenges

Authors: Chundong Wang; Lei Yang; Hao Guo; Fujin Wan

Addresses: Key Laboratory of Computer Vision and System, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Ministry of Education, Tianjin University of Technology, Tianjin, China ' Key Laboratory of Computer Vision and System, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Ministry of Education, Tianjin University of Technology, Tianjin, China ' Global Energy Internet Research Institute, Beijing University of Technology, Tianjin, China ' Department of College of Computer and Control Engineering, Nankai University, Tianjin, China

Abstract: Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks.

Keywords: data protection; security provenance; data masking; data sharing; pseudo-row watermarking.

DOI: 10.1504/IJICS.2020.107449

International Journal of Information and Computer Security, 2020 Vol.12 No.4, pp.416 - 435

Received: 04 Jan 2018
Accepted: 23 Feb 2018
Published online: 29 May 2020 *

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