Abstract
While enjoying the convenience brought by location-based services, mobile users also face the risk of leakage of location privacy. Therefore, it is necessary to protect location privacy. Most existing privacy-preserving methods are based on K-anonymous and L-segment diversity to construct an anonymous set, but lack consideration of the distribution of semantic location on the road segments. Thus, the number of various semantic location types in the anonymous set varies greatly, which leads to semantic inference attack and privacy disclosure. To solve this problem, a privacy-preserving method is proposed based on degree of semantic distribution similarity on the road segment, ensuring the privacy of the anonymous set. Finally, the feasibility and effectiveness of the method are proved by extensive experiments evaluations based on dataset of real road network.
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Acknowledgement
This paper was supported by the National Natural Science Foundation of China under Grant No. 61672039 and 61370050; and the Key Program of Universities Natural Science Research of the Anhui Provincial Department of Education under Grant No. KJ2019A1164.
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Liu, R., Zuo, K., Wang, Y., Zhao, J. (2020). Location Privacy-Preserving Method Based on Degree of Semantic Distribution Similarity. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_9
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DOI: https://doi.org/10.1007/978-981-15-7981-3_9
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