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论文类型：期刊论文

发表时间：2016-04-01

发表刊物：JOURNAL OF MEDICAL SYSTEMS

收录刊物：SCIE、PubMed、Scopus

卷号：40

期号：4

页面范围：97

ISSN号：0148-5598

关键字：Body area networks; Big data; Differential privacy; Dynamic noise thresholds

摘要：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.