RDPP-TD: Reputation and Data Privacy-Preserving based Truth Discovery Scheme in Mobile Crowdsensing
Lijian Wu, Weikun Xie, Wei Tan, Tian Wang, Houbing Herbert Song,, Anfeng Liu

TL;DR
This paper introduces RDPP-TD, a novel scheme in mobile crowdsensing that combines reputation-based truth discovery with privacy preservation, significantly enhancing data quality and privacy protection.
Contribution
The paper proposes a new RDPP-TD scheme integrating reputation and privacy-preserving methods for truth discovery in MCS, improving accuracy and data quality.
Findings
Data quality improved by up to 33.3%.
Effective privacy preservation for sensing data and reputation values.
Supports reputation-based worker recruitment and incentives.
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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Human Mobility and Location-Based Analysis · Opportunistic and Delay-Tolerant Networks
