CPS: Medium: Distorting the adversary's view: a CPS approach to privacy and security

该项目通过协调CPS应用的低延迟和低抖动要求与安全和隐私要求，为无线网络CPS系统开发了一种新颖的以网络物理系统（CPS）为中心的隐私和安全方法。我们首先观察到，在CPS中，对手的主要目标不是学习所有原始数据，而是学习核心属性，例如从数据派生的状态或控制操作。在此观察的基础上，我们建议使用一种失真措施来提高安全性，使窃听者的估计与计算感兴趣属性的函数的真实值之间的差异最大化，从而降低对手破坏CPS正常运行的能力。我们假设可以用比保护所有原始数据所需的资源更少的资源来保护这些核心属性。确保网络CPS系统上的安全和私有信息交换对于构建蓬勃发展的应用生态系统至关重要，这些应用包括从自动驾驶汽车和无人机到物联网（IoT），再到用于健康、教育和协作的增强现实等沉浸式环境。我们的教育计划不仅招收研究生和博士后，而且招收高中生和本科生。它还通过提供我们算法的开源实现，使它们既可供工业界使用，也可供业余爱好者使用，从而接触到工程师和普通公众。该项目考虑了被动攻击和主动攻击。我们将量化基于度量空间中的失真测量的CPS系统的新隐私和安全措施；我们将开发基本边界以及低复杂性和低开销的编码方案；我们将量化主动对手的破坏性力量，设计主动防御机制和事后防御机制；我们将通过旗舰应用程序无人机定位来说明我们的方法。我们的方法将通过设计低延迟、低抖动的CPS要求，为无线网络加密方法提供一种替代方案。

This project develops a novel Cyber Physical System (CPS) centric approach to privacy and security for wireless networked CPS systems, by reconciling the low-delay and low-jitter requirements of CPS applications with the requirements imposed by security and privacy. Our starting observation is that, in CPS, an adversary's primary goal is not to learn all the raw data, but instead core attributes, such as the state or control actions that are derived from data. Building on this observation, we propose to use a distortion measure for security that maximizes the difference between the eavesdropper's estimate and the true value of the function computing the attributes of interest, reducing the adversary's ability to disrupt normal operation of CPS. We posit that we can protect these core attributes with fewer resources than needed to protect all the raw data. Ensuring secure and private information exchange over networked CPS systems is essential to building a thriving ecosystem of applications that range from autonomous cars and drones, to the Internet-of-Things (IoT), to immersive environments such as augmented reality for health, education, and collaboration. Our educational plan engages not only graduate students and postdocs but also high school and undergraduate students. It also reaches out to engineers and the lay public, by providing open source implementations of our algorithms making them available both to industry and hobbyists. The project considers both passive and active attacks. We will quantify novel privacy and security measures for CPS systems that are based on distortion measurements in a metric space; we will develop fundamental bounds as well as low complexity and low overhead coding schemes; we will quantify the disruptive power of active adversaries and design pro-active and retro-active defense mechanisms; and we will illustrate our approach over a flagship application, drone localization. Our approach will offer an alternative to wireless network encryption methods, by designing for low-delay, low-jitter requirements of CPS.