DCA: The Advanced Privacy-Enhancing Schemes for Location-Based Services

  • Jiaxun Hua
  • Yu Liu
  • Yibin Shen
  • Xiuxia TianEmail author
  • Cheqing Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


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.


LBS privacy k-anonymity Confusion degree 



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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jiaxun Hua
    • 1
  • Yu Liu
    • 1
  • Yibin Shen
    • 1
  • Xiuxia Tian
    • 3
    Email author
  • Cheqing Jin
    • 2
  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina
  2. 2.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina
  3. 3.Shanghai University of Electric PowerShanghaiChina

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