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Differential Privacy Preserving in Big Data Analytics for Connected Health

  • Systems-Level Quality Improvement
  • Published:
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Abstract

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.

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Acknowledgments

This research is sponsored in part by the National Natural Science Foundation of China (No.61402078 and No. 61572231). This research is also sponsored in part supported by the Fundamental Research Funds for the Central Universities (No.DUT14RC(3)090).

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, China

    Chi Lin, Zihao Song, Yanhong Zhou, Yi Wang & Guowei Wu

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Chi Lin, Zihao Song, Yanhong Zhou, Yi Wang & Guowei Wu

  3. Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV, 25136, USA

    Houbing Song

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  1. Chi Lin
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  2. Zihao Song
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  3. Houbing Song
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  4. Yanhong Zhou
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  5. Yi Wang
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Corresponding author

Correspondence to Chi Lin.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Lin, C., Song, Z., Song, H. et al. Differential Privacy Preserving in Big Data Analytics for Connected Health. J Med Syst 40, 97 (2016). https://doi.org/10.1007/s10916-016-0446-0

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