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An Enhanced Location-Data Differential Privacy Protection Method Based on Filter

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Smart Grid and Internet of Things (SGIoT 2021)

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Abstract

Location based service (LBS) is the basic function and important application of Internet of things. The disclosure of location data which contains a lot of sensitive information will be a threat for individual. This paper proposed an enhanced location-data differential privacy protection method based on filter. Firstly, noise is added in location-data for differential privacy. Secondly, Kalman is used to predict, correct and optimize the Location-data after the addition of noise, which ensure the optimization to satisfy the differential privacy. Finally, released the processed data and carry out the location query service. Experimental results demonstrate that the proposed algorithm promotes Location-data utility and level of privacy protection.

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Acknowledgment

This work is partially supported by National Social Science Foundation of China (21BTQ079), Humanities and Social Sciences Project of the Ministry of Education (20YJAZH046) and Higher education research projects (2020GJZD02).

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Authors and Affiliations

  1. School of Computer Science, Beijing Information Science and Technology University, Beijing, 100192, China

    Shasha Zhang

  2. School of Information Management, Beijing Information Science and Technology University, Beijing, 100192, China

    Haiyan Kang & Dong Yu

Authors
  1. Shasha Zhang
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  2. Haiyan Kang
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  3. Dong Yu
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Corresponding author

Correspondence to Haiyan Kang .

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Editors and Affiliations

  1. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

    Yi-Bing Lin

  2. Department of Computer Science and Information, National Changhua University of Education, Changhua City, Taiwan

    Der-Jiunn Deng

  3. Department of Computer Science, Tunghai University, Taichung, Taiwan

    Chao-Tung Yang

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Zhang, S., Kang, H., Yu, D. (2022). An Enhanced Location-Data Differential Privacy Protection Method Based on Filter. In: Lin, YB., Deng, DJ., Yang, CT. (eds) Smart Grid and Internet of Things. SGIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-20398-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-20398-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20397-8

  • Online ISBN: 978-3-031-20398-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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