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Anti-data Mining on Group Privacy Information

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Human Centered Computing (HCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10745))

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Abstract

In the big data era, privacy preserving is a vital security challenge for data mining. Common object of privacy preserving is personal privacy, which should be kept unrevealed while data mining on group information. However, for a few sensitive groups, such as suffering from some particular disease, engaging in some special occupation or having some peculiar hobby, even if every personal data is processed for privacy preserving, group specificity can be still exposed. Therefore, we propose the concept and method of anti-data mining on group privacy information. By adding, swapping data according to our rules, the minable characteristic and group specificity of original data is destroyed and eliminated to prevent group privacy from data mining.

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Acknowledgement

The project was supported by the National Natural Science Foundation of China under Grant 61502440 and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP1610.

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Authors and Affiliations

  1. Computer School, China University of Geosciences, Wuhan, 430074, China

    Fan Yang, Tian Tian, Hong Yao, Xiuyu Zhao & Tinggang Zheng

  2. Founder International Software (Beijing) Co., Ltd., Beijing, 100080, China

    Min Ning

Authors
  1. Fan Yang
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  2. Tian Tian
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  3. Hong Yao
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  4. Xiuyu Zhao
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  5. Tinggang Zheng
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  6. Min Ning
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Corresponding author

Correspondence to Xiuyu Zhao .

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Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe Ltd., Hayes, United Kingdom

    Bo Hu

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Yang, F., Tian, T., Yao, H., Zhao, X., Zheng, T., Ning, M. (2018). Anti-data Mining on Group Privacy Information. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_51

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  • DOI: https://doi.org/10.1007/978-3-319-74521-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74520-6

  • Online ISBN: 978-3-319-74521-3

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