A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

ZHOU Yousheng; WANG Zhonghan; LIU Yuanni

doi:10.23919/cje.2023.00.332

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Yousheng ZHOU, Zhonghan WANG, and Yuanni LIU, “A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx doi: 10.23919/cje.2023.00.332

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Yousheng ZHOU, Zhonghan WANG, and Yuanni LIU, “A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx doi: 10.23919/cje.2023.00.332

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A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

doi: 10.23919/cje.2023.00.332

ZHOU Yousheng^{1, 2
,},
WANG Zhonghan^1
,,
LIU Yuanni^{2
,
,}

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, PR China
2.
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, PR China

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Author Bio:
Yousheng ZHOU received the Ph.D. degree from Beijing University of Posts and Telecommunications, in 2011. He is currently a professor with the Chongqing University of Posts and Telecommunications. He has published more than 20 academic papers in peer-reviewed international journals. He is now served as the Associate Editor-in-Chief of Journal of Chongqing University of Posts and Telecommunications. His research interests include data security, privacy in machine Learning etc. (Email: zhouys@cqupt.edu.cn)

Zhonghan WANG received the B.E. degree in Software Engineering from Taiyuan University of Technology, Taiyuan, China, in 2021. He is currently a master student of School of Computer Science and Technology, Chongqing University of Posts and Telecommunications. Her research interest is differential privacy protection. (Email: 1060289861@qq.com)

Yuanni LIU received the M.E. degree from Zhengzhou University, Zhengzhou, China, in 2008, and the Ph.D. degree from the Beijing University of Posts and Telecommunications, Beijing, China, in 2011. She currently serves as a Professor with the School of Cybersecurity and Law, Chongqing University of Posts and Telecommunications, Chongqing, China. Her research interests include mobile crowdsensing, the IoT security, IP routing technology, and complex networks. (Email: liuyn@cqupt.edu.cn)
Corresponding author: Email: liuyn@cqupt.edu.cn
Received Date: 2022-03-22
Accepted Date: 2022-03-22

Available Online: 2024-03-21

Abstract

Abstract

As a new computing method, edge computing not only improves the computing efficiency and processing power of data, but also reduces the transmission delay of data. However, due to the wide variety of edge devices and the increasing amount of terminal data, third-party data centers are unable to ensure that user privacy data leaked. To solve these problems, this paper proposes an iterative clustering algorithm (LDPIA) based on localized differential privacy, which implements LDP local differential privacy. To address the problem of uncertainty in numerical types of mixed data, random perturbation is applied to the user data at the attribute category level. The server then performs clustering on the perturbed data, and density threshold and disturbance probability are introduced to update the cluster point set iteratively.In addition, a new distance calculation formula is defined in combination with attribute weights to ensure the availability of data. The experimental results show that LDPIA algorithm achieves better privacy protection and availability simultaneously.
- edge computing,
- privacy protection,
- local differential privacy,
- attribute weight,
- iterative clustering

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References(30)

References

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