A Differentially Private Method for Crowdsourcing Data Submission

  • Lefeng ZhangEmail author
  • Ping Xiong
  • Tianqing Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. However, these platforms present serious threats to people’s location privacy, because sensitive information may be leaked from submitted spatiotemporal data. 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. Experiments show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.


Privacy preservation Spatial crowdsourcing Differential privacy 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information and Security EngineeringZhongnan University of Economics and LawWuhanChina
  2. 2.School of Information TechnologyDeakin UniversityGeelongAustralia

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