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Practical Issues on Privacy-Preserving Health Data Mining

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Emerging Technologies in Knowledge Discovery and Data Mining (PAKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4819))

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Abstract

Privacy-preserving data mining techniques could encourage health data custodians to provide accurate information for mining by ensuring that the data mining procedures and results cannot, with any reasonable degree of certainty, violate data privacy. We outline privacy-preserving data mining techniques/systems in the literature and in industry. They range from privacy-preserving data publishing, privacy-preserving (distributed) computation to privacy-preserving data mining result release. We discuss their strength and weaknesses respectively, and indicate there is no perfect technical solution yet. We also provide and discuss a possible development framework for privacy-preserving health data mining systems.

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Authors and Affiliations

  1. NICTA, Locked Bag 8001, Canberra ACT, 2601, Australia

    Huidong (Warren) Jin

  2. RSISE, the Australian National University, Canberra ACT, 2601, Australia

    Huidong (Warren) Jin

Authors
  1. Huidong (Warren) Jin
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Editor information

Takashi Washio Zhi-Hua Zhou Joshua Zhexue Huang Xiaohua Hu Jinyan Li Chao Xie Jieyue He Deqing Zou Kuan-Ching Li Mário M. Freire

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Jin, H.(. (2007). Practical Issues on Privacy-Preserving Health Data Mining. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_8

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  • DOI: https://doi.org/10.1007/978-3-540-77018-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77016-9

  • Online ISBN: 978-3-540-77018-3

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