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Privacy Preserving for Big Data Based on Fuzzy Set

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11065))

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Abstract

Today Big Data is one of the major technology usages for every research areas in competitive world. There are many important aspects with Big Data which would be volume, velocity, variety and veracity. Furthermore it is necessary to optimize existing methods to be executable for privacy preserving of Big Data. In this paper, firstly analysis about Big Data and its associated privacy Preserving, then makes an overview of privacy preservation especially for the Location Privacy Data. Furthermore it proposes model for privacy preserving, and then gives formulation about the algorithm of Privacy Preserving Based on Fuzzy Set (PPFS) which can help to achieve privacy preserving.

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Acknowledgements

The research is supported by the National Science Foundation (NSF) under Grants (No. 61602161), Hubei Natural Science Foundation under Grants (No. 2014CFB590), Natural Science Foundation of Hubei University of Technology under Grant (No. BSQD13039), Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things Foundation under Grants (No. 2015III015-A03).

Author information

Authors and Affiliations

  1. Hubei University of Technology, Wuhan, 430068, China

    Jun Wu & Chunzhi Wang

  2. Wuhan University of Technology, Wuhan, 430070, China

    Jun Wu

Authors
  1. Jun Wu
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  2. Chunzhi Wang
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Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Wu, J., Wang, C. (2018). Privacy Preserving for Big Data Based on Fuzzy Set. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_59

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  • DOI: https://doi.org/10.1007/978-3-030-00012-7_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00011-0

  • Online ISBN: 978-3-030-00012-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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