ITR: Privacy and Surveillance In Wireless Networks

ITR：无线网络中的隐私和监控

• 批准号: 0430593
• 负责人: Dirk Grunwald
• 金额: --
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430593&HistoricalAwards=false
• 关键词: ITR; Privacy; Surveillance; Wireless; Networks

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.

该项目对无线网络中的位置隐私进行辩证研究——“好警察”与“坏警察”，探索常见无线数据网络中监视的技术限制和能力。 这项工作将隐私方面的知识现状与概率回归结合起来。无线网络中的当前位置监控与根据射频信息确定位置密切相关；当前增强隐私的技术依赖于通过改变射频信息来欺骗观察者而不影响使用底层网络的能力的能力。该射频信息不精确，并且会受到相当大的环境噪声的影响。或者，隐私增强机制改变媒体访问层的各个方面，例如用于识别站点的唯一密钥，以努力隐藏单个站点并增强隐私。迄今为止，对意外泄露问题的调查很少无线网络中的私人信息。例如，当前的监控技术可以通过不同品牌手机中使用的电子元件的特性来识别在一个区域中使用的唯一品牌的手机。通常，这种复杂的分析需要昂贵的设备。随着越来越多的带宽不受监管，监控此类带宽的能力成为底层技术的一个固有方面。软件定义无线电系统需要并有能力使用多个频率范围并对无线介质的 MAC 和 PHY 层进行控制，新兴的混合可重构系统将加速此类设备的广泛使用。随着这种功能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，该提案资助的研究旨在验证或反驳当前的隐私增强方面研究。这不仅有助于确定现有和拟议的隐私机制的技术限制，而且可能有助于为此类领域的政策制定指导。此外，试图通过先进的统计机器学习侵犯位置隐私的对位研究可能表明无线隐私不能通过技术来强制执行，而必须由政策来规定。这种监视技术本身也可能很有趣，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。所寻求的算法足够简单，可以在软件无线电中嵌入的混合可重构逻辑上运行，从而允许进行多样化且现实的实验和评估。