ITR: Privacy and Surveillance In Wireless Networks

本项目对无线网络中的位置隐私进行了辩证研究--对普通无线数据网络中监视的技术限制和能力进行了“好警察”与“坏警察”的探索。 这项工作结合了隐私知识的现状和概率回归。无线网络中的当前位置监视与从射频信息确定位置整体相关;增强隐私的当前技术依赖于通过改变RF信息而不影响使用底层网络的能力来欺骗观察者的能力。该RF信息是不精确的并且受到相当大的环境噪声的影响。或者，隐私增强机制改变了媒体接入层的各个方面，例如用于识别站点的唯一密钥，以掩盖单个站点并增强隐私性。例如，目前的监测技术可以通过不同品牌的手机中使用的电子部件的特性来识别在一个区域中使用的手机的独特品牌。通常，这种复杂的分析需要昂贵的设备。随着更多的带宽不受管制，监控这些带宽的能力成为基础技术的内在方面。软件定义的无线电系统需要并能够使用多个频率范围并对无线介质的MAC和PHY层进行控制，并且新兴的混合可重配置系统将加速这种设备的广泛使用。随着这些功能强大的手机变得越来越普遍，它们的可编程性将允许更广泛的日益复杂的监控。通过利用机器学习算法和嵌入式系统的组合，这项提案资助的研究旨在验证或反驳当前研究的隐私增强方面。这不仅有助于确定现有和拟议的隐私机制的技术限制，而且还有助于为这些领域的政策制定指导。此外，试图通过先进的统计机器学习来侵犯位置隐私的对比研究可能表明，无线隐私不能通过技术来强制执行，必须由政策来决定。这种监控技术本身也很有趣，可以更准确地跟踪具有嘈杂位置传感器的各方。该研究使用概率回归模型来估计软件无线电收集的无线电数据的轨迹。算法的要求是足够简单的混合可重构逻辑嵌入在软件无线电上运行，允许多样化和现实的实验和评估。

This project conducts a dialectic study of location privacy in wireless networks -- a ``good cop'' vs ``bad cop'' exploration of the technical limits and abilities for surveillance in common wireless data networking. The work combines the current state of knowledge in privacy with probabilistic regression. Current location monitoring in wireless networks is integrally related to determining location from radio frequency information; current techniques to enhance privacy depend on the ability to fool observers by varying that RF information without affecting the ability to use the underlying network. This RF information is imprecise and subject to considerable environmental noise. Alternatively, privacy-enhancing mechanisms vary aspects of the media access layer, such as unique keys used to identify stations, in an effort to cloak an individual station and enhance privacy.To date, there has been little investigation in to the issue of unintended disclosure of private information in wireless networks. For example, current monitoring techniques can identify the unique brand of cellphones being used in a region by characteristics of the electronic components used in different brands of cellphones. Normally, such sophisticated analysis requires expensive equipment. As more bandwidth is unregulated, the ability to monitor such bandwidths becomes an intrinsic aspect of the underlying technology. Software defined radio systems have the need and ability to use a number of frequency ranges and to exercise control over the MAC and PHY layers of the wireless medium, and emerging hybrid reconfigurable systems will accelerate the wide spread use of such devices. As such capable handsets become more commonplace, their very programmability will allow a broader range of increasingly sophisticated surveillance.By exploiting a combination of machine learning algorithms and embedded systems, the research funded by this proposal seeks to verify or refute the privacy enhancing aspects of current research. Not only will this help determine the technological limits to existing and proposed privacy mechanisms, but it may help set guidance for policies in such domains. Moreover, the contrapuntal study that attempts to violate location privacy through advanced statistical machine learning may indicate that wireless privacy can not be enforced by technology, and must be dictated by policy. This surveillance technology may also be interesting in its own right, allowing more accurate tracking of parties that have noisy location sensors. The study uses probabilistic regression models to estimate trajectories from radio data collected by a software radio. The algorithms are sought that are simple enough to run on the hybrid reconfigurable logic embedded in the software radio, allowing diverse and realistic experimentation and evaluation.