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CAREER: New control-theoretic approaches for cyber-physical privacy

CAREER: New control-theoretic approaches for cyber-physical privacy
职业:网络物理隐私的新控制理论方法
批准号:
1846706
负责人:
Minghui Zhu
金额:
$50.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-03-01 至 2025-02-28

项目摘要

项目成果

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中文摘要
翻译
标题:网络物理隐私的新控制论方法先进的信息和通信技术(ICT)正日益渗透到我们的世界。技术进步正在刺激新一代大规模网络物理系统(CP)的迅速出现,包括智能电网、智能建筑、智能交通系统、医疗设备网络和移动机器人网络。CPS由大量地理上分散的实体组成,因此分布式数据共享对于实现全网目标是必要的。然而,分布式数据共享也引起了人们的严重关切,即合法实体的私人或机密信息可能被泄露给未经授权的实体。在某些CP可以广泛部署之前,隐私已成为一个高度优先解决的问题。保护信息和通信技术系统数据隐私的现有技术不足以确保CPS隐私。该项目将开发新的控制理论方案,以确保成功完成大规模CP的控制任务,同时保护合法实体的隐私。该项目的成果将为在敌对操作环境中建立可信赖的CP提供工程指导。拟议的教育和外展活动将有助于培养一批精力充沛的具有多学科背景的熟练专业人员和工程师,以满足CPS的快速增长。本项目将从一个全新的控制理论角度系统地研究CPS隐私。拟议的研究将通过利用两个不同领域的技术工具来开发新的方案:(1)决策和控制(控制理论、优化、博弈论、分布式算法);(2)计算机科学(数据隐私、密码学)。新方案将实现数学上可证明的隐私和控制论性能。研究议程包括三个方面:(I)开发新的同态加密方案来解决分布式优化问题,其中计算是在加密数据上进行的;(Ii)设计对动态网络的输入和输出的反馈扰动,以保护网络隐私,保持系统效用,例如可控性,并将扰动造成的成本降至最低;以及(Iii)通过电力系统、智能建筑和机器学习的案例研究来评估所开发的理论。该项目将涉及与联邦、军事和工业部门的研究实验室合作。这些合作将促进技术转让,并产生超出学术界的影响。这项研究的成功完成将发现动态系统在CPS隐私中所扮演的独特角色,对控制理论和数据隐私之间的相互作用提供新的理解,并使CPS能够以可信的方式运行。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Title: New control-theoretic approaches for cyber-physical privacyAdvanced information and communications technologies (ICT) are increasingly permeating through our world. The technological advances are stimulating the rapid emergence of new-generation large-scale cyber-physical systems (CPS), including the smart grid, smart buildings, intelligent transportation systems, medical device networks and mobile robotic networks. CPS consists of a large number of geographically dispersed entities and thus distributed data sharing is necessary to achieve network-wide goals. However, distributed data sharing also raises the significant concern that the private or confidential information of legitimate entities could be leaked to unauthorized entities. Privacy has become an issue of high priority to address before certain CPS can be widely deployed. Existing techniques to protect the data privacy of ICT systems are not sufficient to ensure CPS privacy. This project will develop new control-theoretic schemes to assure the successful completion of control tasks for large-scale CPS and simultaneously preserve the privacy of legitimate entities. The outcomes of this project will provide engineering guidelines to build trustworthy CPS in adversarial operating environments. The proposed activities on education and outreach will contribute to training an energetic generation of skilled professionals and engineers with multidisciplinary background to satisfy the rapid growth of CPS.This project will systematically study CPS privacy from a fresh control-theoretic perspective. The proposed research will develop new schemes by leveraging technical tools of two disparate domains: (1) decision and control (control theory, optimization, game theory, distributed algorithms); (2) computer science (data privacy, cryptography). The new schemes will achieve mathematically provable privacy and control-theoretic performance. The research agenda consists of three thrusts: (i) developing new homomorphic encryption schemes to solve distributed optimization problems where the computation is carried over encrypted data; (ii) designing feedback perturbations on the inputs and outputs of dynamic networks such that network privacy is protected, system utilities; e.g., controllability, are maintained, and the costs caused by the perturbations are minimized; and (iii) evaluating the developed theory using the case studies of power systems, smart buildings and machine learning. The project will involve collaborations with research laboratories in federal, military and industrial sectors. The collaborations will promote technology transfer and make an impact beyond academia. The successful completion of this research will discover the unique role dynamic systems play in CPS privacy, provide the new understandings of the interplay between control theory and data privacy, and enable CPS to operate in a trustworthy manner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DOI: --
发表时间: 2022
期刊:
影响因子: --
作者: [Xu Zhang;Zhenyuan Yuan;Minghui Zhu]
通讯作者: Xu Zhang;Zhenyuan Yuan;Minghui Zhu
DOI: 10.1007/s10994-022-06215-7
发表时间: 2022-07
期刊: Machine Learning
影响因子: 7.5
作者: [S. Saab;S. Phoha;Minghui Zhu;A. Ray]
通讯作者: S. Saab;S. Phoha;Minghui Zhu;A. Ray
Federated reinforcement learning for generalizable motion planning
用于泛化运动规划的联合强化学习
DOI: 10.23919/acc55779.2023.10156236
发表时间: 2023
期刊: 2023 American Control Conference
影响因子: --
作者: [Yuan, Zhenyuan, Xu, Siyuan, Zhu, Minghui]
通讯作者: Zhu, Minghui
DOI: 10.1016/j.arcontrol.2019.04.010
发表时间: 2019
期刊: Annu. Rev. Control.
影响因子: --
作者: [Yang Lu;Minghui Zhu]
通讯作者: Yang Lu;Minghui Zhu
共 8 条
    Towards Provable Security of Real-world Servers: Where Online Learning Meets Server Retrofitting
    Data-driven distributed control of mobile robotic networks: Where machine learning meets game theory
    Breakthrough: CPS-Security: Towards Provably Correct Distributed Attack-Resilient Control of Unmanned-Vehicle-Operator Networks
    海外基金