EAGER: Control Theory for Real-time Privacy-preserving Consensus Control of Engineering Networks

EAGER:工程网络实时隐私保护共识控制的控制理论

基本信息

  • 批准号:
    1824014
  • 负责人:
  • 金额:
    $ 11.16万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-05-01 至 2019-01-31
  • 项目状态:
    已结题

项目摘要

This EArly-concept Grant for Exploratory Research (EAGER) project considers how well-understood control architectures can be modified to produce novel consensus control strategies in the context of privacy protection for engineering networks. Communication and computing capabilities are increasingly being integrated into even the most commonplace items, forming an interconnected network of devices capable of independent and cooperative action. These connected objects can coordinate their actions to improve the efficiency and performance of systems such as smart power grids and automated traffic control systems. However, the widespread sharing of data also risks divulging private information. For example, the exchange of very detailed consumer usage profiles between network operators and dispatch units enables optimized power forecast, generation, and distribution in a smart grid, but also allows others to infer knowledge about the presence, absence, or even the specific activities of a home's occupants. Classical encryption is too slow to be deployed in time-critical engineering applications such as self-driving cars, where carefully tuned controllers generate tens or hundreds of commands per second. The insight developed in this project is that exchanged information, and even control commands, can be obscured by the innovative application of existing control architectures. This project will demonstrate the feasibility of this approach and outline its major features. The results of this project will advance national prosperity by offering the economic advantages of communication and coordination, without violating confidentiality. The research results will be integrated into the graduate curriculum, and into undergraduate research projects supervised by the Principal Investigator.This project will explore the stabilization of decentralized coordination algorithms under random time-varying coupling weights which will be used for obscuring information. With recent advances in wireless communications and networking, decentralized coordination algorithms have become widespread in networked robots, sensor networks, and intelligent transportation systems. Although their inherent flexibility and scalability make decentralized coordination algorithms appealing for large-scale systems, they also pose significant challenges to privacy protection design. This is because conventional privacy-preserving mechanisms rely on the assistance of a central data aggregator or a trusted third party -- mechanisms that are ruled out in a decentralized implementation. This project builds upon recent results by the Investigator, specifically a privacy-preserving mechanism which obscures information through uncertain time-varying controls. The approach does not need the assistance of any third party and is superior to existing approaches in terms of flexibility, scalability, accuracy, and computation overhead. However, this privacy-preserving approach leads to random time-varying coupling weights, whose influence on decentralized coordination is unclear. This project will address this problem through rigorous analysis of the convergence conditions and speed of decentralized coordination under random time-varying coupling weights. The main thrusts of the project are first to characterize the condition under which nonlinear consensus can be achieved when coupling weights are time-varying and randomly chosen from a certain interval, next to analyze the convergence speed of decentralized coordination under random time-varying coupling weights, and finally to systematically verify obtained results using a multi-robot test bed.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.
这个早期概念的探索性研究资助(EAGER)项目考虑如何修改已充分理解的控制架构,以在工程网络的隐私保护背景下产生新型共识控制策略。通信和计算能力越来越多地被集成到甚至最普通的物品中,形成能够独立和协作动作的设备的互连网络。这些连接的对象可以协调它们的动作,以提高智能电网和自动交通控制系统等系统的效率和性能。然而,数据的广泛共享也有泄露私人信息的风险。例如,网络运营商和调度单元之间非常详细的消费者使用简档的交换实现了智能电网中的优化的电力预测、发电和分配,而且还允许其他人推断关于家庭居住者的存在、不存在或甚至特定活动的知识。经典加密速度太慢,无法部署在自动驾驶汽车等时间紧迫的工程应用中,其中精心调整的控制器每秒生成数十或数百个命令。在这个项目中开发的洞察力是,交换的信息,甚至控制命令,可以通过现有的控制架构的创新应用模糊。本项目将证明这一方法的可行性,并概述其主要特点。该项目的成果将通过提供沟通和协调的经济优势,在不违反保密规定的情况下促进国家繁荣。研究成果将被纳入研究生课程,并纳入由首席研究员监督的本科生研究项目。该项目将探讨分散协调算法在随机时变耦合权重下的稳定性,该权重将用于模糊信息。随着无线通信和网络的最新进展,分散式协调算法已广泛应用于网络化机器人、传感器网络和智能交通系统。虽然分散式协调算法固有的灵活性和可扩展性使其对大规模系统具有吸引力,但它们也对隐私保护设计提出了重大挑战。这是因为传统的隐私保护机制依赖于中央数据聚合器或受信任的第三方的帮助-这些机制在去中心化实现中被排除在外。该项目建立在研究人员最近的研究结果基础上,特别是通过不确定的时变控制来掩盖信息的隐私保护机制。该方法不需要任何第三方的帮助,并且在灵活性、可扩展性、准确性和计算开销方面上级现有方法。然而,这种隐私保护的方法导致随机时变耦合权重,其对分散协调的影响尚不清楚。本项目将通过严格分析随机时变耦合权重下分散协调的收敛条件和速度来解决这个问题。该项目的主要工作是首先刻画了当耦合权值为时变且在一定区间内随机选取时非线性协调一致的条件,然后分析了随机时变耦合权值下分散协调的收敛速度,最后,系统地验证所获得的结果,该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Privacy-Preserving Average Consensus via State Decomposition
Secure and Privacy-Preserving Consensus
  • DOI:
    10.1109/tac.2019.2890887
  • 发表时间:
    2017-07
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Minghao Ruan;Huan Gao;Yongqiang Wang
  • 通讯作者:
    Minghao Ruan;Huan Gao;Yongqiang Wang
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Yongqiang Wang其他文献

Gold-Catalyzed Cyclization/Hydroboration of 1,6-Enynes: Synthesis of Bicyclo[3.1.0]hexane Boranes
金催化 1,6-烯炔的环化/硼氢化:双环[3.1.0]己烷硼烷的合成
  • DOI:
    10.1021/acs.orglett.2c03812
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Guanghui Wang;Yongqiang Wang;Zengzeng Li;Haotian Li;Mingwu Yu;Maofu Pang;Ximei Zhao
  • 通讯作者:
    Ximei Zhao
A Compact SISL Balun Using Compensated Interdigital Capacitor
使用补偿叉指电容器的紧凑型 SISL 巴伦
Embryonic and larval development in barfin flounder Verasper moseri (Jordan and Gilbert)
条鳍鲽 Verasper moseri 的胚胎和幼体发育(Jordan 和 Gilbert)
  • DOI:
    10.1007/s00343-010-9251-7
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Rongbin Du;Yongqiang Wang;Haibin Jiang;Liming Liu;Maojian Wang;Tianbao Li;Shubao Zhang
  • 通讯作者:
    Shubao Zhang
Effect of nonmagnetic substitution on the magnetic correlation of the frustrated Ca3CoMn1-xGaxO6 (0≤x≤0.2)
非磁性取代对受挫Ca3CoMn1-xGaxO6 (0≤x≤0.2)磁相关性的影响
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    6.2
  • 作者:
    Gaoshang Gong;Chenfei Shi;Jinjin Guo;Gebru Zerihun;Yongqiang Wang;Yang Qiu;Yuling Su
  • 通讯作者:
    Yuling Su
Reversal and non-reversal ferroelectric polarizations in a Y-type hexaferrite
Y 型六角形铁氧体中的反转和非反转铁电极化
  • DOI:
    10.1039/c8tc05247f
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    Yongqiang Wang;Shile Zhang;W. K. Zhu;Langsheng Ling;Lei Zhang;Zhe Qu;Li Pi;Wei Tong;Mingliang Tian
  • 通讯作者:
    Mingliang Tian

Yongqiang Wang的其他文献

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{{ truncateString('Yongqiang Wang', 18)}}的其他基金

CIF: Small: Ensuring Accuracy in Differentially Private Decentralized Optimization
CIF:小:确保差分隐私去中心化优化的准确性
  • 批准号:
    2334449
  • 财政年份:
    2024
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
FRR: Collaborative Research: Collaborative Learning for Multi-robot Systems with Model-enabled Privacy Protection and Safety Supervision
FRR:协作研究:具有模型支持的隐私保护和安全监督的多机器人系统协作学习
  • 批准号:
    2219487
  • 财政年份:
    2022
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
CIF: Small: Deep Stochasticity for Private Collaborative Deep Learning
CIF:小:私人协作深度学习的深度随机性
  • 批准号:
    2215088
  • 财政年份:
    2022
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Medium: Harnessing Intrinsic Dynamics for Inherently Privacy-preserving Decentralized Optimization
合作研究:CIF:中:利用内在动力学实现固有隐私保护的去中心化优化
  • 批准号:
    2106293
  • 财政年份:
    2021
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Continuing Grant
Encrypted control for privacy-preserving and secure cyber-physical systems
隐私保护和安全网络物理系统的加密控制
  • 批准号:
    1912702
  • 财政年份:
    2019
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
CICI: RSARC: Secure Time for Cyberinfrastructure Security
CICI:RSARC:网络基础设施安全的安全时间
  • 批准号:
    1738902
  • 财政年份:
    2017
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
STTR Phase I: Eco-Friendly Mass Production of Highly Conductive Graphene Sheets with Controlled Structures
STTR第一阶段:结构可控的高导电石墨烯片的环保大规模生产
  • 批准号:
    1346496
  • 财政年份:
    2014
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
STTR Phase I: Surface- and Structural Engineering of Colloidal Quantum Dots Towards Efficient and
STTR 第一阶段:胶体量子点的表面和结构工程,以实现高效和
  • 批准号:
    1010491
  • 财政年份:
    2010
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
STTR Phase I: Magnetic Nanoparticle Microfluidics for High Efficient Capture, Separation and Concetration of Foodborne Pathogens
STTR 第一阶段:用于高效捕获、分离和浓缩食源性病原体的磁性纳米颗粒微流体
  • 批准号:
    0810626
  • 财政年份:
    2008
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant
SBIR Phase II: Development of Cadmium-Free, Water-Soluble and Multicolor Quantum Dots by Chemical Doping
SBIR 第二阶段:通过化学掺杂开发无镉、水溶性和多色量子点
  • 批准号:
    0823040
  • 财政年份:
    2008
  • 资助金额:
    $ 11.16万
  • 项目类别:
    Standard Grant

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Cortical control of internal state in the insular cortex-claustrum region
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