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pDCS: Security and Privacy Support for Data-Centric Sensor Networks

机译:pDCS:对以数据为中心的传感器网络的安全性和隐私支持

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摘要

The demand for efficient data dissemination/access techniques to find relevant data from within a sensor network has led to the development of data-centric sensor (DCS) networks, where the sensor data instead of sensor nodes are named based on attributes such as event type or geographic location. However, saving data inside a network also creates security problems due to the lack of tamper resistance of the sensor nodes and the unattended nature of the sensor network. For example, an attacker may simply locate and compromise the node storing the event of his interest. To address these security problems, we present pDCS, a privacy-enhanced DCS network which offers different levels of data privacy based on different cryptographic keys. pDCS also includes an efficient key management scheme to facilitate the management of multiple types of keys used in the system. In addition, we propose several query optimization techniques based on Euclidean Steiner tree and keyed bloom filter (KBF) to minimize the query overhead while preserving query privacy. Finally, detailed analysis and simulations show that the KBF scheme can significantly reduce the message overhead with the same level of query delay and maintain a very high level of query privacy.
机译:对于从传感器网络中查找相关数据的高效数据分发/访问技术的需求,导致了以数据为中心的传感器(DCS)网络的发展,其中基于事件类型等属性来命名传感器数据而不是传感器节点或地理位置。但是,由于缺少传感器节点的防篡改功能以及传感器网络的无人值守特性,将数据保存在网络内部还会产生安全问题。例如,攻击者可能会简单地定位并破坏存储其感兴趣事件的节点。为了解决这些安全问题,我们提出了pDCS,这是一种隐私增强的DCS网络,它基于不同的加密密钥提供不同级别的数据隐私。 pDCS还包括一个有效的密钥管理方案,以方便管理系统中使用的多种类型的密钥。此外,我们提出了几种基于欧氏Steiner树和键布隆过滤器(KBF)的查询优化技术,以在保持查询隐私的同时最大程度地减少查询开销。最后,详细的分析和仿真表明,KBF方案可以在相同的查询延迟级别下显着减少消息开销,并保持很高的查询隐私级别。

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