WTIPPTD: Weight-Based Trust Identification for Privacy-Preserving Truth Discovery in MCS

In Mobile Crowd Sensing (MCS), how to obtain accurate truth estimation under privacy preservation has gained much attention. It is important to prevent the leakage of the sensing data, weight, estimated truth, and intermediate truth to third parties to avoid attacks from adversaries when aggregating...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-07, p.1-1
Main Authors: Yu, Shiyuan, Deng, Qingyong, Liu, Haolin, Peng, Xin, Xie, Yong
Format: Article
Language:English
Subjects:
Accuracy
Data integrity
Data privacy
Mobile Crowd Sensing
Privacy
Privacy-preserving
Sensors
Servers
Task analysis
Trust
Truth discovery
Worker Recruitment
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Summary:In Mobile Crowd Sensing (MCS), how to obtain accurate truth estimation under privacy preservation has gained much attention. It is important to prevent the leakage of the sensing data, weight, estimated truth, and intermediate truth to third parties to avoid attacks from adversaries when aggregating a large amount of data collected by workers. In addition, dishonest or malicious workers may report false or malicious data. Therefore, we propose a Weight-Based Trust Identification for Privacy-Preserving Truth Discovery (WTIPPTD) scheme to enhance the accuracy of truth discovery by identifying the trust and data qualities of workers, and then recruiting trusted high-quality workers. First, the Garbled Circuit (GC) is used for weight update in the encrypted state, and a trust evaluation scheme is proposed based on weight credibility. Second, a data quality evaluation scheme for workers is designed, and the trusted high-quality workers are recruited to improve the accuracy of truth discovery while reducing the recruitment cost. Finally, we conduct experiments with a large number of real and synthetic datasets, and the results show that our proposed scheme significantly improves the accuracy of truth discovery by 17.80% 98.61% and substantially reduces worker recruitment cost by 16.43% 26.50%.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3436626