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A differential privacy protection scheme for sensitive big data in body sensor networks

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Abstract

As a special kind of application of wireless sensor networks, body sensor networks (BSNs) have broad application perspectives in health caring. Big data acquired from BSNs usually contain sensitive information, such as physical condition, location information, and so on, which is compulsory to be appropriately protected. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, a differential privacy protection scheme for sensitive big data in BSNs is proposed. A tree structure is constructed to reduce errors and provide long range queries. Haar Wavelet transformation method is applied to convert histogram into a complete binary tree. At last, to verify the advantages of our scheme, several experiments are conducted to show the outperformed results. Experimental results demonstrate that the tree structure greatly reduces the calculation overheads which preserves differential privacy for users.

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Acknowledgments

This research is sponsored in part by the National Natural Science Foundation of China (No.61402078 and 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, Pengyu Wang, Yanhong Zhou, Qing Liu & Guowei Wu

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

    Chi Lin, Pengyu Wang, Yanhong Zhou, Qing Liu & Guowei Wu

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

    Houbing Song

Authors
  1. Chi Lin
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  2. Pengyu Wang
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  3. Houbing Song
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  4. Yanhong Zhou
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  5. Qing Liu
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  6. Guowei Wu
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Correspondence to Chi Lin.

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Lin, C., Wang, P., Song, H. et al. A differential privacy protection scheme for sensitive big data in body sensor networks. Ann. Telecommun. 71, 465–475 (2016). https://doi.org/10.1007/s12243-016-0498-7

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  • DOI: https://doi.org/10.1007/s12243-016-0498-7

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