Abstract
Privacy protection and quality of LBS is a pair of contradictions. How to improve quality of LBS while ensuring privacy requirements is worthy of study. In continuous queries, users may have different privacy requirements in different situations, so we propose two algorithms to address this problem, in which before satisfying privacy requirements, they expand cloaking area that contains least users according to the pyramid structure and user’s moving trend respectively. Experimental results show that, the two algorithms both satisfy user’s personalized privacy requirements, and reduce cloaking area, thus improve the query efficiency, especially the latter algorithm, where the cloaking area is nearly one sixth as large as that of the compared algorithms in the best situation.
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Acknowledgements
The work was partially supported by the Natural Science Foundation of China (No. 61272403), by the Fundamental Research Funds for the Central Universities (No. 10561201474).
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Gan, J., Xu, H., Xu, M., Tian, K., Zheng, Y., Zhang, Y. (2016). Study on Personalized Location Privacy Protection Algorithms for Continuous Queries in LBS. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10066. Springer, Cham. https://doi.org/10.1007/978-3-319-49148-6_9
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DOI: https://doi.org/10.1007/978-3-319-49148-6_9
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