Differential Privacy Preserving in Big Data Analytics for Connected Health

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• 作者：Chi Lin ; Zihao Song ; Houbing Song ; Yanhong Zhou ; Yi Wang…
• 关键词：Body area networks ; Big data ; Differential privacy ; Dynamic noise thresholds
• 刊名：Journal of Medical Systems
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：40
• 期：4
• 全文大小：2,145 KB
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• 作者单位：Chi Lin (1) (2)
  Zihao Song (1) (2)
  Houbing Song (3)
  Yanhong Zhou (1) (2)
  Yi Wang (1) (2)
  Guowei Wu (1) (2)
  
  1. School of Software, Dalian University of Technology, Dalian, China
  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
  3. Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV, 25136, USA
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Health Informatics and Administration
  
• 出版者：Springer Netherlands
• ISSN：1573-689X

文摘

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. Keywords Body area networks Big data Differential privacy Dynamic noise thresholds