计算机科学
声誉
可靠性(半导体)
Paillier密码体制
基本事实
移动设备
移动计算
信息隐私
任务(项目管理)
数据挖掘
计算机安全
人工智能
万维网
计算机网络
公钥密码术
加密
社会学
物理
混合密码体制
经济
功率(物理)
量子力学
管理
社会科学
作者
Yudan Cheng,Jianfeng Ma,Zhiquan Liu,Zhetao Li,Yongdong Wu,Caiqin Dong,Runchuan Li
出处
期刊:IEEE Transactions on Dependable and Secure Computing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-18
标识
DOI:10.1109/tdsc.2023.3276976
摘要
In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this paper proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.
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