The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis

ZHOU Yihui; WANG Wenli; YAN Jun; WU Zhenqiang; LU Laifeng

doi:10.23919/cje.2023.00.272

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Article Navigation > Chinese Journal of Electronics > 2024 > Accepted Manuscript

Yihui ZHOU, Wenli WANG, Jun YAN, et al., “The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–15, xxxx doi: 10.23919/cje.2023.00.272

Citation:

Yihui ZHOU, Wenli WANG, Jun YAN, et al., “The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–15, xxxx doi: 10.23919/cje.2023.00.272

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The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis

doi: 10.23919/cje.2023.00.272

ZHOU Yihui^1
,,
WANG Wenli^2
,,
YAN Jun^{1, 3
,},
WU Zhenqiang^1
,,
LU Laifeng^{2
,
,}

1.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2.
School of Mathematics and Statistics, Shaanxi Normal University, Xi’an 710119, China
3.
School of Mathematics and Computer Applications, Shangluo College, Shangluo 726000, China

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Author Bio:
Yihui ZHOU received her B.E. degree, M.S. degree and Ph.D. degree in College of Mathematics and Information Science from Shaanxi Normal University, Shaanxi, China, in 2003, in 2006 and in 2009, respectively. Now she is a lecturer in School of Computer Science, Shaanxi Normal University. Her research interests include information security and privacy preserving. (Email: zhouyihui@snnu.edu.cn)

Wenli WANG is a postgraduate of Shaanxi Normal University. Her main research interest is privacy protection. (Email: wangwenli@snnu.edu.cn)

Jun YAN received the M.S. degree in College of Earth Exploration Science and Technology, Jilin University. He is currently pursuing the Ph.D. degree in School of Computer Science, Shaanxi Normal University. His research interests include network security and privacy preserving. (Email: yanrongjunde@snnu.edu.cn)

Zhenqiang WU is a Professor in the School of Computer Science, Shaanxi Normal University, Xi’an, China. He obtained his Ph.D. degree in Computer Science and Application from School of Computer Science and Technology, Xidian University, China in 2007. His main research interests include computer networks, network security, network coding and its applications. (Email: zqiangwu@snnu.edu.cn)

Laifeng LU received her B.S. degree 2001 from Shaanxi Normal University and received her M.S. and Ph.D. degrees in 2005 and 2012 respectively, all from Xidian University. She is currently a full associate professor and master’s supervisor of Shaanxi Normal University. Her research interests include network security and privacy protection. She is a member of CCF and Privacy Protection Committee. (Email: lulaifeng@snnu.edu.cn)
Corresponding author: Email: lulaifeng@snnu.edu.cn
Available Online: 2024-03-21

Abstract

Abstract

Currently, it has become a consensus to enhance privacy protection. Randomized response (RR) technique, as the mainstream perturbation mechanism for local differential privacy, has been widely studied. However, most of the research in literature managed to modify existing RR schemes and propose new mechanisms with better privacy protection and utility, which are illustrated only by numerical experiments. We study the properties of generalized binary randomized response mechanisms from the perspectives of Lanke privacy and utility. The mathematical expressions of privacy and utility for the binary RR mechanism are given respectively. Moreover, the comparison principle for privacy and utility of any two mechanisms is proved. Finally, the optimization problem of the binary RR mechanism is discussed. Our work is based on a rigorous mathematical proof of privacy and utility for the general binary RR mechanism, and numerical verification illustrates the correctness of the conclusions. It can provide theoretical support for the design of binary RR mechanism and can be applied in data collection, analysis and publishing.
- Randomized response,
- Lanke privacy,
- Utility optimization

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References(26)

References

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