Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

WANG Zhiwei; ZHU Qiuchi; ZHANG Zhenqi

doi:10.23919/cje.2022.00.257

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WANG Zhiwei, ZHU Qiuchi, ZHANG Zhenqi, “Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.257, 2022.

Citation:

WANG Zhiwei, ZHU Qiuchi, ZHANG Zhenqi, “Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.257, 2022.

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

doi: 10.23919/cje.2022.00.257

WANG Zhiwei^{1, 2, 3, 4
,},
ZHU Qiuchi¹,
ZHANG Zhenqi¹

1.
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China
3.
Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu University of Information Engineering, Chengdu 610225, China
4.
Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou 571158, China

Funds: Thanks for Mr. Keyu Yan for doing some experiments in this paper! This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province under Grant No. SKLACSS-202114, the Foundation of State Key Laboratory of Public Big data, and the Open Fund of Key Laboratory of Computational Science and Application of Hainan Province under Grant No.JSKX202202.

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Author Bio:
Zhiwei WANG received the PhD degree in cryptography from the Beijing University of Posts and Telecommunications, Beijing, China, in 2009. Since 2009, he has been a professor with the School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China. He was a research associate with the University of Hong Kong from March 2014 to March 2015. He has published more than 50 journal articles and referred conference papers. His research interests include applied cryptography, security and privacy in mobile and wireless systems, clouding computing, and fog/edge computing. (Email: zhwwang@njupt.edu.cn)

Qiuchi ZHU received the Bachelor’s degree in Information Security from the Nanjing University of Posts and Telecommunications in 2022. She is currently a master student at the school of computer, Nanjing University of Posts and Telecommunications. Her research interest is applied cryptography

Zhenqi ZHANG received the Bachelor’s degree from the Nanjing University of Posts and Telecommunications in 2022. He is currently a master student at the school of computer, Nanjing University of Posts and Telecommunications. His research interest is cryptographic protocol
Received Date: 2022-03-22
Accepted Date: 2022-03-22

Available Online: 2023-07-20

Abstract

Abstract

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a WiFi fingerprint localization system based on Paillier encryption, which is claimed to protect both client $C$’s location privacy and service provider $S$’s database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain $S$’s database. In this paper, we propose a novel WiFi fingerprint localization system based on CL encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.’s attack, the system requires that $S$ selects only the locations from its database that can receive the nonzero signals from all the available APs in $C$’s nonzero fingerprint in order to determine $C$’s location. Security analysis shows that our scheme is secure under Li et al.’s threat model. Furthermore, to enhance the security level of PriWFLCL, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in $C$’s encrypted localization queries.
- WiFi fingerprint-based localization,
- CL encryption,
- HSM group,
- zero-knowledge proof

$ R_{i\gamma} $ is computed as $ R_{i\gamma}=\frac{d_i^0-d_a^0-d_i^1+d_a^1}{2} $ in Yang et al.’s attack.

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References(27)

References

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