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Homogeneous Compressive Sensing for Privacy Preservation

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When Compressive Sensing Meets Mobile Crowdsensing

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Abstract

From Chaps. 4 and 5, we have learnt how compressive sensing and its extended versions effectively reconstruct incomplete data set and help tackling the data quality maintenance problem in MCS. In this chapter, we will turn around and see a new topic: privacy preservation problem. The key problem of this chapter is how to preserve the privacy without impacting the accuracy of data reconstruction.

This chapter is represented with permission from ©[2015] IEEE ref. Kong, L., He, L., Liu, X.Y., Gu, Y., Wu, M.Y. and Liu, X., 2015, June. Privacy-preserving compressive sensing for crowdsensing based trajectory recovery. In IEEE 35th International Conference on Distributed Computing Systems (pp. 31–40).

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Linghe Kong

  2. ByteDance Ltd., Beijing, China

    Bowen Wang

  3. Department of Computer Science, Nanjing University, Nanjing, China

    Guihai Chen

Authors
  1. Linghe Kong
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  2. Bowen Wang
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  3. Guihai Chen
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Corresponding author

Correspondence to Linghe Kong .

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Kong, L., Wang, B., Chen, G. (2019). Homogeneous Compressive Sensing for Privacy Preservation. In: When Compressive Sensing Meets Mobile Crowdsensing. Springer, Singapore. https://doi.org/10.1007/978-981-13-7776-1_6

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  • DOI: https://doi.org/10.1007/978-981-13-7776-1_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7775-4

  • Online ISBN: 978-981-13-7776-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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