Information leaks out: Attacks and countermeasures on compressive data gathering in wireless sensor networks
Information leaks out: Attacks and countermeasures on compressive data gathering in wireless sensor networks
复制标题
DOI：
10.1109/infocom.2014.6848058
复制
发表时间：
2014-07
期刊：
影响因子：
--
通讯作者：
Pengfei Hu;Kai Xing;Xiuzhen Cheng;Hao Wei;Haojin Zhu
中科院分区：
文献类型：
--
作者：
Pengfei Hu;Kai Xing;Xiuzhen Cheng;Hao Wei;Haojin Zhu
压缩感知（CS）被认为是一种很有前途的技术，可以大大提高无线传感器网络中数据收集的通信效率。然而，这种新的数据收集模式可能会带来新的威胁，但很少有研究注意防止信息泄漏压缩数据收集过程中。在本文中，我们确定了两种统计推断攻击，并证明了传统的压缩数据收集可能会遭受严重的信息泄漏下，这些攻击。在理论分析中，通过大量的统计分析定量分析了压缩数据聚集的估计误差，在此基础上，提出了一种新的安全压缩数据聚集方案，该方案通过自适应地改变每个传感器和相应的接收端的测量系数，而无需时间同步。在我们的分析中，我们表明，所提出的方案可以显着提高数据的机密性在轻的计算和通信开销。
Compressive sensing (CS) has been viewed as a promising technology to greatly improve the communication efficiency of data gathering in wireless sensor networks. However, this new data collection paradigm may bring in new threats but few study has paid attention to prevent information leakage during compressive data gathering. In this paper, we identify two statistical inference attacks and demonstrate that traditional compressive data gathering may suffer from serious information leakage under these attacks. In our theoretical analysis, we quantitatively analyze the estimation error of compressive data gathering through extensive statistical analysis, based on which we propose a new secure compressive data aggregation scheme by adaptively changing the measurement coefficients at each sensor and correspondingly at the sink without the need of time synchronization. In our analysis, we show that the proposed scheme could significantly improve data confidentiality at light computational and communication overhead.