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Source Anonymity in WSNs against Global Adversary Utilizing Low Transmission Rates with Delay Constraints

机译：WSN中针对全球对手的源匿名（利用具有延迟约束的低传输速率）

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Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.
机译：无线传感器网络（WSN）已部署到许多应用程序中，例如跟踪和监视濒危物种，军事应用程序等，这些应用程序要求起源的匿名性，即源位置隐私（SLP）。 SLP的目的是通过分析网络中的流量来防止未经授权的观察者跟踪真实事件的来源。诸如强化匿名通信协议（FACP）之类的SLP先前方法在每个时隙中都使用真实或伪数据包的传输，这效率很低。为克服此缺点，我们开发了本文介绍的三种不同技术。开发了虚拟统一分发（DUD），虚拟自适应分发（DAD）和受控虚拟自适应分发（CAD），以克服针对全球对手（具有分析和监视整个网络的能力）的匿名性问题。当前大多数技术都试图阻止对手感知真实事件的位置和时间，而我们提出的技术通过引入低速率伪造消息使对手对真实事件的存在感到困惑，从而导致位置和时间隐私。仿真结果表明，所提出的技术提供了合理的传递比率，延迟和真实事件数据包的开销，同时保持了较高的匿名性。进行了三种不同的分析模型以验证我们技术的性能。进行仿真数据的可视化以确认匿名性。此外，开发了神经网络模型以确保引入的技术保留SLP。最后，建立了基于概率的隐写术模型以证明技术的匿名性。

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