Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Wang Ren, Xin Tong, Jing Du, Na Wang, Shancang Li (), Geyong Min and Zhiwei Zhao
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Wang Ren: Sichuan University
Xin Tong: China Information Technology Security Evaluation Center
Jing Du: China Information Technology Security Evaluation Center
Na Wang: China Information Technology Security Evaluation Center
Shancang Li: University of the West of England
Geyong Min: University of Exeter
Zhiwei Zhao: University of Electronic Science and Technology of China

Information Systems Frontiers, 2024, vol. 26, issue 6, No 12, 2227-2238

Abstract: Abstract The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

Keywords: Privacy preserving; Internet of things (IoT); Industrial 4.0; Data anonymisation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10796-021-10116-w

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