Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing

Deng Qingyong; Zuo Qinghua; Li Zhetao; Wang En; Guo Bin

doi:10.7544/issn1000-1239.202440302

Journal of Computer Research and Development > 2024 > 61(11): 2681-2692. > DOI: 10.7544/issn1000-1239.202440302 CSTR: 32373.14.issn1000-1239.202440302

Deng Qingyong, Zuo Qinghua, Li Zhetao, Wang En, Guo Bin. Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing[J]. Journal of Computer Research and Development, 2024, 61(11): 2681-2692. DOI: 10.7544/issn1000-1239.202440302

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Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing

1.
Key Lab of Education Blockchain and Intelligent Technology (Guangxi Normal University), Ministry of Education, Guilin, Guangxi 541004
2.
College of Information Science and Technology, Jinan University, Guangzhou 510632
3.
Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012
4.
College of Computer Science, Northwestern Polytechnical University, Xi’an 710129

Funds: This work was supported by the Guangxi Natural Science Foundation (2023GXNSFDA026003), the National Natural Science Foundation of China (62076214, 62032020, U23B2027), and the Innovation Project of Guangxi Graduate Education (XYCSR2024100).

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Author Bio:
Deng Qingyong: born in 1981. PhD, associate professor, master supervisor. Senior member of IEEE and CCF. His main research interests include IoT, AI, and network security

Zuo Qinghua: born in 2001. Master. His main research interests include crowdsensing and blockchain

Li Zhetao: born in 1980. PhD, professor, PhD supervisor. His main research interests include IoT, crowdsensing, and network security

Wang En: born in 1987. PhD, professor. His main research interests include mobile computing, mobile crowdsensing, and delay tolerant network

Guo Bin: born in 1980. PhD, professor. His main research interests include ubiquitous computing, mobile crowdsensing, and HCI
Received Date: April 25, 2024
Revised Date: July 30, 2024
Available Online: August 13, 2024

Graphical Abstract

Abstract

Abstract

Reputation management is an effective way to improve the quality of data collection in mobile crowdsensing, but existing subjective evaluation-based schemes are difficult to realize the real evaluation of worker reputation under privacy protection. To this end, we propose a bilateral reputation evaluation privacy-preserving (BREPP) scheme based on blockchain. The scheme firstly adopts random interference and Pedersen Commitment to hide the parameters of the workers’ reputation update, and utilizes the compact linkable spontaneous anonymous group (CLSAG) signature to anonymously verify the reputation update process. Besides, it employs a combination of Bulletproofs and Schnnor signature to verify the reputation feedback process of the workers. Then, the sub-address method is adopted to achieve fast location retrieval when the identities of the workers and requesters are anonymized. Finally, a prototype system is deployed on Hyperledger Fabric to evaluate the performance of the reputation update. The simulation results show that compared with existing reputation privacy protection schemes, BREPP scheme reduces the total reputation management delay and reputation evaluation error rate by at least 2.00% and 24.96%, respectively. Compared with schemes without privacy protection, our scheme achieves a better balance between reputation evaluation accuracy and total delay.
- mobile crowdsensing,
- reputation management,
- blockchain,
- privacy preserving,
- anonymous verification

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References (32)

References

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