LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing
- Publisher:
- SPRINGER HEIDELBERG
- Publication Type:
- Journal Article
- Citation:
- Journal of Ambient Intelligence and Humanized Computing, 2022, 13, (12), pp. 5803-5818
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| s12652-021-03266-x.pdf | Published version | 4.08 MB |
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While spatial crowdsourcing has become a popular paradigm for spatio-temporal data collection, location privacy has raised increasing concerns among the participants of spatial crowdsourcing projects in recent years. The question of how to implement a spatial crowdsourcing project at minimal cost while preserving location privacy, is the major issue that most existing works have investigated. In this paper, we propose a novel privacy-preserving method for spatial crowdsourcing that combines location obfuscation and path optimization in order to provide enhanced privacy preservation at a minimal cost. We apply geo-indistinguishability and exponential mechanism to achieve an enhanced privacy guarantee. Moreover, because a higher privacy level consistently leads to extra distance cost, we therefore present a path optimization algorithm that reduces the total distance of a spatial crowdsourcing project. The experimental results demonstrate that the proposed method outperforms the traditional methods in terms of privacy level and performance costs.
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