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CHEN Qiuling, YE Ayong, ZHANG Qiang, HUANG Chuan. A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IoT[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.411
Citation: CHEN Qiuling, YE Ayong, ZHANG Qiang, HUANG Chuan. A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IoT[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.411

A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IoT

doi: 10.1049/cje.2021.00.411
Funds:  This work is supported by the National Natural Science Foundation of China (No.61972096, No.61872090, No.61771140, No.61872088).
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  • Author Bio:

    was born in 1990. She is a Ph.D. candidate of the College of Computer and Cyber Security at Fujian Normal University, Fuzhou, China. Her research interests include blockchain, network security and location privacy

    (corresponding author) was born in 1977. He received the Ph.D. degree in computer system architecture from Xidian University, Xi’an, China, in 2009. He is currently a professor and PhD supervisor of the College of Computer and Cyber Security at Fujian Normal University, China. His research interests include blockchain, network security and location privacy. (Email: yay@fjnu.edu.cn)

    was born in 1990. She is a Ph.D. candidate of the College of Computer and Cyber Security at Fujian Normal University, Fuzhou, China. Her research interests include blockchain, network security and location privacy

    was born in 1979. He received the Ph.D. degree in communication from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2015. He is currently a lecturer of the College of Computer and Cyber Security at Fujian Normal University, China. His research interests include network attacks and information security

  • Available Online: 2022-03-09
  • A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of Things (IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users’ sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users’ sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.
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