CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks
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Tang, Jianheng, Kejia Fan, Shihao Yang, Anfeng Liu, Neal N. Xiong, Houbing Herbert Song, and Victor C. M. Leung. “CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks.” IEEE Journal on Selected Areas in Communications, 2025, 1–1. https://doi.org/10.1109/JSAC.2025.3560038.
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UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Benchmark testing Computer science Crowdsensing Data collection Data privacy Data Privacy Data Quality Internet of Things Next-Generation Crowdsensing Network Recruitment Security Smart cities Vehicle dynamics Worker Recruitment Zero-Trust
Benchmark testing Computer science Crowdsensing Data collection Data privacy Data Privacy Data Quality Internet of Things Next-Generation Crowdsensing Network Recruitment Security Smart cities Vehicle dynamics Worker Recruitment Zero-Trust
Abstract
Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches.