A differentially private method for crowdsourcing data submission
- Publication Type:
- Journal Article
- Citation:
- Concurrency Computation, 2019, 31 (19)
- Issue Date:
- 2019-10-10
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Zhang_et_al-2019-Concurrency_and_Computation__Practice_and_Experience.pdf | Published Version | 1.54 MB |
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© 2018 John Wiley & Sons, Ltd. 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.
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