A differentially private method for crowdsourcing data submission. (21st December 2018)

• Record Type: Journal Article
• Authors: Zhang, Lefeng; Xiong, Ping; Ren, Wei; Zhu, Tianqing
• Other Names: Wyrzykowski Roman guestEditor.; Szymanski Boleslaw guestEditor.; Pan Lei guestEditor.; Zhang Jun guestEditor.; Oliver Jonathan guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 19(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-21
• Subjects: differential privacy -- privacy preservation -- spatial crowdsourcing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5100
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. In spatial crowdsourcing, workers contribute to a project by performing a task at a specific location. However, these platforms present serious threats to people's location privacy because sensitive information may be leaked from submitted spatiotemporal data. As a result, people may be hesitant to join spatial crowdsourcing projects, which hampers further applications of this business model. In this paper, we propose a private spatial crowdsourcing data submission algorithm, called PS‐Sub. This is a differentially private method that preserves people's location privacy and provides acceptable data utility. Rigorous privacy analyses theoretically demonstrate the privacy guarantees inherent in the proposed model. Experiments based on real‐world datasets were conducted using practical evaluation metrics. The results show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.