Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints

IEEE Transactions on Information Forensics and Security(2014)

引用 41|浏览22
暂无评分
摘要
Wireless bodyworn sensing devices are fast becoming popular for fitness, sports training, and personalized healthcare applications. Securing data generated by these devices is essential if they are to be integrated into the current health infrastructure and employed in medical applications. In this paper, we propose a mechanism to secure the data provenance for these devices by exploiting spatio-temporal characteristics of the wireless channel that these devices use for communication. Our solution enables two parties to generate closely matching link fingerprints, which uniquely associate a data session with a wireless link such that a third party can later verify the details of the transaction, particularly the wireless link on which the data was transmitted. These fingerprints are very hard for an eavesdropper to forge; they are lightweight compared with traditional provenance mechanisms and enable interesting security properties such as accountability, nonrepudiation, and resist man-in-the-middle attacks. We validate our technique with experiments using bodyworn sensors in scenarios approximating actual device deployment and present some extensions, which reduce energy consumption. We believe this is a promising first step toward using wireless-link characteristics for the data provenance in body area networks.
更多
查看译文
关键词
body area networks,health care,radio links,telecommunication security,wireless channels,body area networks,bodyworn sensors,closely matching link fingerprints,data session,device deployment,energy consumption,first-hop data provenance,fitness,health infrastructure,medical applications,personalized healthcare applications,security properties,spatio-temporal characteristics,sports training,wireless bodyworn sensing devices,wireless channel,wireless link fingerprints,Body area networks,data provenance,physical layer security
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要