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A Personalized Preservation Mechanism Satisfying Local Differential Privacy in Location-Based Services

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Security and Privacy in Digital Economy (SPDE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1268))

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Abstract

With the wide application of location-based services, there is a huge amount of users’ spatial data generated by mobile devices every day. However, the data is left from mobile users and faced with leakage risk from adversaries or untrusted data receivers. Therefore, spatial data should be perturbed to satisfy local differential privacy (LDP), which is a strong privacy metric in the local setting. In this paper, we study the problem of designing a personalized mechanism satisfying LDP for spatial data. We first construct attack and defense for privacy of spatial data and give a novel privacy definition with LDP and users’ personalized requirements. We propose a personalized location privacy preservation mechanism for spatial data satisfying LDP. We demonstrate the optimal utility and privacy guarantee of our mechanism. We analyze the impact of the key parameters on data utility via the experiments over the real dataset.

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Acknowledgment

This research was partially supported by the National Key Research and Development Program of China (No. 2017YFB1400600).

Author information

Authors and Affiliations

  1. Changzhou University, Changzhou, China

    Datong Wu

  2. School of Computer Science and Technology, Nanjing Normal University, Nanjing, China

    Xiaotong Wu, Jiaquan Gao & Genlin Ji

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Xiaolong Xu

  4. School of Information, Science and Engineering, Qufu Normal University, Rizhao, China

    Lianyong Qi

  5. The State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Wanchun Dou

Authors
  1. Datong Wu
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  2. Xiaotong Wu
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  3. Jiaquan Gao
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  4. Genlin Ji
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  5. Xiaolong Xu
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  6. Lianyong Qi
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  7. Wanchun Dou
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Corresponding author

Correspondence to Xiaotong Wu .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shui Yu

  2. IBM Zurich Research Laboratory, Zurich, Switzerland

    Peter Mueller

  3. Ningbo University, Ningbo, China

    Jiangbo Qian

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Wu, D. et al. (2020). A Personalized Preservation Mechanism Satisfying Local Differential Privacy in Location-Based Services. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_12

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  • DOI: https://doi.org/10.1007/978-981-15-9129-7_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9128-0

  • Online ISBN: 978-981-15-9129-7

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