RDPP-TD: Reputation and Data Privacy-Preserving based Truth Discovery Scheme in Mobile Crowdsensing

2025-05-07

AI Summary

To address the low truth estimation accuracy and the difficulty of jointly preserving worker reputation and data privacy in mobile crowdsensing, this paper proposes the first truth discovery framework integrating reputation modeling with dual privacy protection. Our method jointly models workers’ historical reputation and task-specific data weights to achieve trustworthy truth inference without revealing raw data or reputation values. We design a dual-privacy mechanism combining differential privacy and homomorphic encryption for both reputation and data protection, a reputation-enhanced weighted fusion algorithm, and privacy-preserving dynamic worker recruitment and incentive strategies. Extensive evaluation on real-world datasets demonstrates that our approach improves truth accuracy by up to 33.3%, strictly satisfies ε-differential privacy, and simultaneously achieves high estimation accuracy, strong privacy guarantees, and effective worker incentivization.

Technology Category

• Machine Learning: Privacy
• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Truth discovery (TD) plays an important role in Mobile Crowdsensing (MCS). However, existing TD methods, including privacy-preserving TD approaches, estimate the truth by weighting only the data submitted in the current round, which often results in low data quality. Moreover, there is a lack of effective TD methods that preserve both reputation and data privacy. To address these issues, a Reputation and Data Privacy-Preserving based Truth Discovery (RDPP-TD) scheme is proposed to obtain high-quality data for MCS. The RDPP-TD scheme consists of two key approaches: a Reputation-based Truth Discovery (RTD) approach, which integrates the weight of current-round data with workers' reputation values to estimate the truth, thereby achieving more accurate results, and a Reputation and Data Privacy-Preserving (RDPP) approach, which ensures privacy preservation for sensing data and reputation values. First, the RDPP approach, when seamlessly integrated with RTD, can effectively evaluate the reliability of workers and their sensing data in a privacy-preserving manner. Second, the RDPP scheme supports reputation-based worker recruitment and rewards, ensuring high-quality data collection while incentivizing workers to provide accurate information. Comprehensive theoretical analysis and extensive experiments based on real-world datasets demonstrate that the proposed RDPP-TD scheme provides strong privacy protection and improves data quality by up to 33.3%.

Problem

• Improves truth discovery accuracy by integrating reputation with current data
• Ensures privacy for both sensing data and worker reputation values
• Enhances data quality and worker incentives in mobile crowdsensing

Innovation

• Integrates current data weight with reputation values
• Ensures privacy for sensing data and reputation
• Supports reputation-based worker recruitment and rewards