Abstract
Location-Based Services (LBS) have garnered significant attention in recent years, emphasizing the need to improve location services while safeguarding user privacy. In this paper, we propose an effective privacy-preserving and enhanced dummy location scheme specifically designed for semi-trusted third-party scenarios, with a primary focus on defending against inference attacks targeting a user’s private location information. To achieve more effective location privacy preservation and mitigate privacy leaks stemming from a single point of failure, we employ a key information sharing mechanism, introduce a robust dummy location set generation approach, and present a comprehensive covering area construction strategy. To demonstrate the viability and effectiveness of our proposed scheme, we conduct a thorough simulation evaluation and performance analysis based on a practical road network setting.
This work is supported in part by the National Key Research and Development Project under Grant 2019YFC0605102, by the National Natural Science Foundation of China under Grant 41972307 and Grant 61672029.
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Zuo, M., Peng, L., Song, J. (2024). An Effective Privacy-Preserving and Enhanced Dummy Location Scheme for Semi-trusted Third Parties. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_14
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DOI: https://doi.org/10.1007/978-981-97-2390-4_14
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