Privacy-Preserving Truth Discovery of Evolving Truths for Mobile Crowdsensing Systems

Jingcheng Zhao, Kaiping Xue, Ruidong Li, Bin Zhu, Meng Li, Qibin Sun, Jun Lu

Abstract: Privacy-preserving truth discovery (PPTD) enables the crowdsensing platform to extract reliable inferred truths from unreliable user sensory data. While mobile crowdsensing systems have driven the emergence of many applications, continuously extracting inferred truths of evolving objects over streaming data (continuous PPTD) remains a challenge. Most existing works focus on static scenarios and cannot handle the new challenges in continuous PPTD, such as accuracy decrease, user dynamics, real-time requirements, and outliers. To address these challenges, we present PTET, a PPTD framework for continuous PPTD. By mining evolving patterns, PTET extracts accurate inferred truths of evolving objects even when some epochs lack sufficient user sensory data. PTET ensures the privacy of both users and data requesters while achieving high accuracy. Furthermore, we present PTET-P for practical applications. It employs a virtual user combined with evolving patterns to effectively eliminate the impact of user dynamics in continuous PPTD. Meanwhile, PTET-P achieves “immediate on-arrival processing” to improve real-time performance significantly. In addition, we address the outliers problem with the help of evolving patterns. We provide security analysis to prove that our frameworks protect the privacy of both users and data requesters. Extensive experiments demonstrate that our frameworks dramatically outperform the existing schemes in extracting inferred truths of evolving objects in continuous PPTD.

External IDs:dblp:journals/tdsc/ZhaoXLZLSL25