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

CPS：中：扭曲对手的观点：隐私和安全的 CPS 方法

• 批准号: 1740047
• 负责人: Christina Fragouli
• 金额: $ 97.76万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740047&HistoricalAwards=false
• 关键词: CPS; Medium; Distorting; adversary; view

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.

该项目开发了一种新的网络物理系统（CPS）为中心的方法，以隐私和安全的无线网络CPS系统，通过协调CPS应用程序的低延迟和低抖动的要求与安全和隐私的要求。我们的初步观察是，在CPS中，对手的主要目标不是学习所有原始数据，而是核心属性，例如从数据中派生的状态或控制操作。基于这一观察，我们建议使用一个失真的安全措施，最大限度地提高窃听者的估计和计算感兴趣的属性的函数的真实值之间的差异，降低对手的能力，破坏CPS的正常运行。我们认为，我们可以用比保护所有原始数据所需的更少的资源来保护这些核心属性。确保通过联网CPS系统进行安全和私密的信息交换对于构建蓬勃发展的应用生态系统至关重要，这些应用包括自动驾驶汽车和无人机、物联网（IoT）以及用于健康、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涉及研究生和博士后，还涉及高中和本科生。它还通过提供我们算法的开源实现来接触工程师和普通公众，使其可用于行业和业余爱好者。该项目考虑了被动和主动攻击。我们将量化CPS系统的新隐私和安全措施，这些措施基于度量空间中的失真测量;我们将开发基本边界以及低复杂度和低开销的编码方案;我们将量化活跃对手的破坏力，并设计主动和追溯防御机制;我们将说明我们的方法在旗舰应用中，无人机定位。 我们的方法将提供一种替代无线网络加密方法，通过设计CPS的低延迟，低抖动的要求。

项目成果

Privacy Against Adversarial Classification in Cyber-Physical Systems

网络物理系统中针对对抗性分类的隐私

• DOI: 10.1109/cdc42340.2020.9303960
• 发表时间: 2020
• 期刊: 2020 59th IEEE Conference on Decision and Control (CDC
• 作者: Murguia, Carlos;Tabuada, Paulo
• 通讯作者: Tabuada, Paulo

Privacy in Index Coding: $k$ -Limited-Access Schemes

索引编码中的隐私：$k$ - 有限访问方案

• DOI: 10.1109/tit.2019.2957577
• 发表时间: 2020
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Karmoose, Mohammed;Song, Linqi;Cardone, Martina;Fragouli, Christina
• 通讯作者: Fragouli, Christina

Symmetries and privacy in control over the cloud: uncertainty sets and side knowledge *

云控制中的对称性和隐私：不确定性集和辅助知识*

• DOI: 10.1109/cdc40024.2019.9029609
• 发表时间: 2019
• 期刊: Conference on Decision and Control (CDC
• 作者: Sultangazin, Alimzhan;Tabuada, Paulo
• 通讯作者: Tabuada, Paulo

Shuffled model of differential privacy in federated learnin

联邦学习中差分隐私的洗牌模型

• 发表时间: 2021
• 期刊: AISTATS
• 作者: Girgis, Antonious M;Data, Deepesh;Diggavi, Suhas;Kairouz, Peter;and Suresh, Theerta.
• 通讯作者: and Suresh, Theerta.

Hiding Identities: Estimation Under Local Differential Privacy

隐藏身份：本地差分隐私下的估计

• DOI: 10.1109/isit44484.2020.9174332
• 发表时间: 2020
• 期刊: IEEE International Symposium on Information Theory (ISIT
• 作者: Girgis, Antonious M.;Data, Deepesh;Diggavi, Suhas
• 通讯作者: Diggavi, Suhas