首页> 外文会议>2010 Proceedings IEEE INFOCOM >Group Device Pairing based Secure Sensor Association and Key Management for Body Area Networks
【24h】

Group Device Pairing based Secure Sensor Association and Key Management for Body Area Networks

机译:基于组设备配对的人体区域网络的安全传感器关联和密钥管理

获取原文

摘要

Body Area Networks (BAN) is a key enabling technology in E-healthcare such as remote health monitoring. An important security issue during bootstrap phase of the BAN is to securely associate a group of sensor nodes to a patient, and generate necessary secret keys to protect the subsequent wireless communications. Due to the the ad hoc nature of the BAN and the extreme resource constraints of sensor devices, providing secure, fast, efficient and user-friendly secure sensor association is a challenging task. In this paper, we propose a lightweight scheme for secure sensor association and key management in BAN. A group of sensor nodes, having no prior shared secrets before they meet, establish initial trust through group device pairing (GDP), which is an authenticated group key agreement protocol where the legitimacy of each member node can be visually verified by a human. Various kinds of secret keys can be generated on demand after deployment. The GDP supports batch deployment of sensor nodes to save setup time, does not rely on any additional hardware devices, and is mostly based on symmetric key cryptography, while allowing batch node addition and revocation. We implemented GDP on a sensor network testbed and evaluated its performance. Experimental results show that that GDP indeed achieves the expected design goals.
机译:人体局域网(BAN)是电子医疗保健中的关键启用技术,例如远程健康监控。 BAN的引导阶段期间的一个重要安全问题是将一组传感器节点安全地关联到患者,并生成必要的密钥以保护后续的无线通信。由于BAN的特殊性质和传感器设备的极端资源限制,提供安全,快速,高效和用户友好的安全传感器关联是一项艰巨的任务。在本文中,我们提出了一种用于BAN中安全传感器关联和密钥管理的轻量级方案。一组传感器节点在相遇之前没有事先共享的秘密,它们通过组设备配对(GDP)建立了初始信任,该设备配对是一种经过身份验证的组密钥协议协议,其中每个成员节点的合法性都可以由人类直观地验证。部署后可以按需生成各种秘密密钥。 GDP支持传感器节点的批量部署,以节省设置时间,不依赖于任何其他硬件设备,并且主要基于对称密钥加密,同时允许添加和撤消批量节点。我们在传感器网络测试平台上实施了GDP,并评估了其性能。实验结果表明,GDP确实达到了预期的设计目标。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号