We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Differential Privacy Preserving in Big Data Analytics for Connected Health.
- Authors
Lin, Chi; Song, Zihao; Song, Houbing; Zhou, Yanhong; Wang, Yi; Wu, Guowei
- 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.
- Subjects
DATA encryption; DATABASE management; INFORMATION retrieval; MEDICAL ethics; PATIENT monitoring; PRIVACY; WEARABLE technology; ACCESS to information; DATA security; DATA analytics
- Publication
Journal of Medical Systems, 2016, Vol 40, Issue 4, p1
- ISSN
0148-5598
- Publication type
Article
- DOI
10.1007/s10916-016-0446-0