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
• 负责人: Yongqiang Wang
• 金额: $ 59.99万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334449&HistoricalAwards=false
• 关键词: CIF; Small; Ensuring; Accuracy; Differentially

Advances in wireless communications and low-cost computing devices have enabled a proliferation of large distributed networks of data collection systems, constituting a major component of the emergent Internet of Things (IoT), Intelligent Transportation Systems (ITS), and the Smart Grid (SG) . Complementing these advances is the significant progress in decentralized optimization software that enables the basic functionalities of such distributed networked systems, including cooperative control, network information fusion, network coordination, and distributed data mining/learning. However, information sharing over such large networks creates vulnerabilities and concerns about privacy, which can be especially acute in privacy-sensitive applications such as smart metering and connected vehicle networks. Differential privacy is the most widely used protective mechanism for privacy due to its simplicity, scalability, and strong resilience against attempts to recover sensitive information from post-processed data. However, all existing differential-privacy solutions for decentralized optimization face the dilemma of how to achieve data privacy protection by compromising the optimizer's speed of convergence rather than its accuracy. This project leverages on the PI’s recent discovery that it is possible to achieve differential privacy guarantees without compromising utility by leveraging on the optimizer's speed of convergence rather than its accuracy. Specifically, the project will establish theoretical and algorithmic foundations for the problem of ensuring differential privacy in decentralized optimization without losing provable optimality. In addition to broadly enabling more effective privacy protections for decentralized networks, the project will impact education by enriching the current curriculum on control and networked systems, and training undergraduate and graduate students in interdisciplinary information privacy research and its applications. This project will establish theoretical and algorithmic foundations for ensuring differential privacy in decentralized optimization algorithms without losing provable optimality. The main research thrusts are to: (1) Investigate the sacrifice in convergence speed in differentially private decentralized optimization with provable optimality using an information-theoretic approach; (2) Explore and establish differential privacy without losing provable optimality in decentralized online optimization, where data are not pre-collected before implementing the algorithm but rather are acquired in a sequential manner; (3) Explore and establish differential privacy without losing probable optimality in decentralized optimization algorithms subject to shared coupling constraints among participating agents’ decision variables; (4) Explore and establish differential privacy in decentralized Nash games (which are essentially decentralized optimization problems with noncooperative agents) without losing provable optimality; (5) Evaluate obtained results using numerical simulations as well as experiments on real-word distributed systems in smart grids and networked intelligent vehicles. This project is jointly funded by Core Program of the Computing and Communication Foundations Division (CCF) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

无线通信和低成本计算设备的进步使得大型分布式数据收集系统网络激增，构成新兴物联网 (IoT)、智能交通系统 (ITS) 和智能电网 (SG) 的主要组成部分与这些进步相辅相成的是去中心化优化软件的重大进步，它实现了此类分布式网络系统的基本功能，包括协作控制、网络信息融合、网络协调和分布式数据挖掘/学习。差异隐私由于其简单性、可扩展性和针对尝试的强大弹性而成为最广泛使用的隐私保护机制。然而，所有现有的去中心化优化的差分隐私解决方案都面临着如何通过损害优化器的收敛速度而不是其准确性来实现数据隐私保护的困境。 PI 最近发现，通过利用优化器的收敛速度而不是准确性，可以在不影响效用的情况下实现差分隐私保证。具体来说，该项目将为去中心化优化中确保差分隐私而不丢失的问题奠定理论和算法基础。除了广泛地为去中心化网络提供更有效的隐私保护外，该项目还将通过丰富当前的控制和网络系统课程以及对本科生和研究生进行跨学科信息隐私培训来影响教育。该项目将为确保去中心化优化算法中的差分隐私而不失去可证明的最优性奠定理论和算法基础，主要研究重点是：（1）研究具有可证明的最优性的差分隐私去中心化优化中收敛速度的牺牲。使用信息论方法；（2）在去中心化在线优化中探索和建立差分隐私，而不会失去可证明的最优性，其中数据不是在实现算法之前预先收集的，而是在算法中获取的。 (3) 探索和建立受参与主体决策变量之间共享耦合约束的去中心化优化算法的差分隐私（本质上是去中心化优化问题）； （5）利用数值模拟以及智能电网和网络智能汽车中的实际分布式系统的实验来评估所获得的结果。该项目由计算和网络核心计划联合资助。通信基金会部门 (CCF) 和刺激竞争性研究既定计划 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。