A Differentially Private Method for Crowdsourcing Data Submission
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.
KeywordsPrivacy preservation Spatial crowdsourcing Differential privacy
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