A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

Bing Han, Qiang Fu, Hongyu Su, Cheng Chi, Chuan Zhang () and Jing Wang
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Bing Han: China National Institute of Standardization, Beijing 100191, China
Qiang Fu: China National Institute of Standardization, Beijing 100191, China
Hongyu Su: China National Institute of Standardization, Beijing 100191, China
Cheng Chi: China Academy of Information and Communications Technology, Beijing 100191, China
Chuan Zhang: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Jing Wang: State Key Laboratory of Networking and Switching Technology, Beijing University of Post and Telecommunications, Beijing 100876, China

Mathematics, 2024, vol. 12, issue 19, 1-20

Abstract: In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.

Keywords: crowdsensing; privacy-preserving; quality-aware reputation; user selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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