Abstract
With the popularity of Location-based Services, LBS providers have been obtaining more data, by analyzing which they may infer users’ real locations and patterns of behavior. Unfortunately, most previous schemes using k-anonymity can hardly resist such fiercer side information-based privacy attacks. To address existing problems, we design a novel metric to accurately measure the resulted privacy level. Additionally, Dual Cloaking Anonymity (DCA) and enhanced-DCA (enDCA) algorithms, which are based on our metric, are also proposed. The former (DCA) constructs a k-anonymity set via carefully selecting k-1 users according to various query probabilities of each area and correlations between users’ query preferences. Then, enDCA further employs caching and location blurring to enhance the privacy preservation. Evaluations show that our proposals can significantly improve the privacy level.
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Acknowledgment
Our research is supported by the National Key Research and Development Program of China (2016YFB1000905), NSFC (61772327, 61370101, 61532021, U1501252, U1401256 and 61402180), Shanghai Knowledge Service Platform Project (No. ZF1213), Shanghai Science and Technology Committee Grant (15110500700).
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Hua, J., Liu, Y., Shen, Y., Tian, X., Jin, C. (2018). DCA: The Advanced Privacy-Enhancing Schemes for Location-Based Services. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_6
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DOI: https://doi.org/10.1007/978-3-319-96893-3_6
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