ITR: Privacy and Surveillance In Wireless Networks

这个项目对无线网络中的位置隐私进行了辩证的研究--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”和“坏警察”的探索。该工作将隐私知识的当前状态与概率回归相结合。无线网络中的当前位置监控与根据射频信息确定位置紧密相关；增强私密性的当前技术依赖于通过改变RF信息来欺骗观察者的能力，而不影响使用底层网络的能力。这种射频信息是不精确的，并且容易受到相当大的环境噪声的影响。或者，隐私增强机制在媒体接入层的不同方面有所不同，例如用于识别站点的唯一密钥，以努力掩盖单个站点并增强隐私。例如，目前的监测技术可以通过不同品牌手机使用的电子元件的特征来识别一个地区使用的独特品牌手机。通常情况下，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，监控此类带宽的能力成为基础技术的一个固有方面。软件定义的无线电系统需要和有能力使用多个频率范围，并对无线介质的MAC层和物理层进行控制，而新兴的混合可重构系统将加速此类设备的广泛使用。随着这类功能强大的手机变得越来越常见，它们本身的可编程性将允许更广泛的、越来越复杂的监控。通过利用机器学习算法和嵌入式系统的组合，这项由该提案资助的研究试图验证或驳斥当前研究中增强隐私的方面。这不仅有助于确定现有和拟议的隐私机制的技术限制，还可能有助于为这些领域的政策设定指导。此外，试图通过高级统计机器学习来侵犯位置隐私的对应性研究可能表明，无线隐私不能由技术强制执行，必须由政策规定。这种监控技术本身也可能很有趣，可以更准确地跟踪有噪音位置传感器的各方。这项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计轨迹。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，允许进行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.