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Secure data aggregation in wireless sensor networks: A watermark based authentication supportive approach

机译:无线传感器网络中的安全数据聚合:基于水印的身份验证支持方法

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In-network processing presents a critical challenge for data authentication in wireless sensor networks (WSNs). Current schemes relying on Message Authentication Code (MAC) cannot provide natural support for this operation since even a slight modification to the data invalidates the MAC. Although some recent works propose using privacy homomorphism to support in-network processing, they can only work for some specific query-based aggregation functions, e.g. SUM, average, etc. In this paper, based on digital watermarking, we propose an end-to-end, statistical approach for data authentication that provides inherent support for in-network processing. In this scheme, authentication information is modulated as watermark and superposed on the sensory data at the sensor nodes. The watermarked data can be aggregated by the intermediate nodes without incurring any en route checking. Upon reception of the sensory data, the data sink is able to authenticate the data by validating the watermark, thereby detecting whether the data has been illegitimately altered. In this way, the aggregation-survivable authentication information is only added at the sources and checked by the data sink, without any involvement of intermediate nodes. Furthermore, the simple operation of watermark embedding and complex operation of watermark detection provide a natural solution of function partitioning between the resource limited sensor nodes and the resource abundant data sink. In addition, the watermark can be embedded in both spatial and temporal domains to provide the flexibility between the detection time and detection granularity. The simulation results show that the proposed scheme can successfully authenticate the sensory data with high confidence.
机译:网络内处理为无线传感器网络(WSN)中的数据身份验证提出了关键挑战。当前依赖于消息认证码(MAC)的方案无法为该操作提供自然支持,因为即使对数据进行很小的修改也会使MAC失效。尽管最近的一些工作提出使用隐私同态来支持网络内处理,但它们只能用于某些特定的基于查询的聚合功能,例如SUM,平均值等。在本文中,我们基于数字水印技术,提出了一种端到端的统计数据身份验证方法,为网络内处理提供了固有的支持。在该方案中,认证信息被调制为水印,并叠加在传感器节点的传感数据上。带水印的数据可以由中间节点聚合,而无需进行任何途中检查。在接收到感官数据时,数据接收器能够通过验证水印来认证数据,从而检测数据是否已经被非法修改。通过这种方式,可在聚合中生存的身份验证信息仅在源处添加,并由数据接收器检查,而无需中间节点的参与。此外,水印嵌入的简单操作和水印检测的复杂操作提供了资源有限的传感器节点与资源丰富的数据宿之间的功能划分的自然解决方案。另外,水印可以嵌入在空间和时间域中,以提供检测时间和检测粒度之间的灵活性。仿真结果表明,所提方案能够成功地对感知数据进行高可信度的认证。

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