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了辩证研究 - ``好警察''与``不良警察''探索了对通用无线数据网络监视的技术限制和能力的探索。 该作品将隐私的当前知识状态与概率回归结合在一起。无线网络中的当前位置监视与从射频信息确定位置的位置始终相关；当前增强隐私的技术取决于通过改变RF信息而不影响使用基础网络的能力来欺骗观察者的能力。此RF信息不精确，并且会遇到相当大的环境噪声。另外，隐私增强机制各种方面的各个方面，例如用于识别电台的独特密钥，以掩盖单个站点并增强隐私性。到目前为止，几乎没有调查过无关紧要的无线网络中私人信息的问题。例如，当前的监测技术可以通过在不同品牌的手机中使用的电子组件的特征来识别该区域中使用的独特品牌。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监管，监视此类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具有使用多种频率范围并对无线介质的Mac和Phy层进行控制的能力，而新兴的混合型可重新配置系统将加速此类设备的广泛扩展。由于这种有能力的手机变得越来越普遍，因此它们的可编程性将允许更广泛的越来越复杂的监视。通过利用机器学习算法和嵌入式系统的结合，该提案资助的研究旨在验证或反驳当前研究的隐私方面。这不仅有助于确定现有和拟议的隐私机制的技术限制，而且还可以帮助在此类领域制定政策指导。此外，试图通过先进的统计机器学习侵犯位置隐私的对立研究可能表明，技术无法执行无线隐私，必须由政策决定。这种监视技术本身也可能很有趣，从而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从软件无线电收集的无线电数据中估算轨迹。寻求的算法非常简单，可以在软件无线电中嵌入的混合重新配置逻辑上运行，从而允许多样化和现实的实验和评估。