Abstract
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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This work is supported by the National Natural Science Foundation of China under Grant No. 61972366, and the Humanities and Social Sciences Planning Project of the China Ministry of Education under Grant No. 19YJAZH099.
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Xiong, P., Li, G., Ren, W. et al. LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing. J Ambient Intell Human Comput 13, 5803–5818 (2022). https://doi.org/10.1007/s12652-021-03266-x
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DOI: https://doi.org/10.1007/s12652-021-03266-x