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数据收集系统的大型分布式网络能够激增，构成新兴的物联网（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展，该软件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。然而，在这样的大型网络上共享信息会产生漏洞和对隐私的担忧，这在智能计量和联网车辆网络等隐私敏感应用中尤其严重。差分隐私是最广泛使用的隐私保护机制，由于其简单性，可扩展性和强大的弹性，以防止试图从后处理数据中恢复敏感信息。然而，所有现有的分散式优化的差分隐私解决方案都面临着如何通过牺牲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。该项目利用PI最近的发现，即通过利用优化器的收敛速度而不是其准确性，可以在不影响效用的情况下实现差分隐私保证。具体来说，该项目将为在分散优化中确保差分隐私而不失去可证明的最优性的问题建立理论和算法基础。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来影响教育，并对本科生和研究生进行跨学科信息隐私研究及其应用的培训。该项目将建立理论和算法基础，以确保分散优化算法中的差分隐私，而不会失去可证明的最优性。主要研究内容包括：（1）利用信息论方法研究具有可证明最优性的差分隐私分散优化算法的收敛速度损失;（2）在分散在线优化算法中探索和建立不损失可证明最优性的差分隐私算法，其中数据不是在算法实现之前预先收集的，而是以顺序的方式获取的;（3）在分布式优化算法中探索和建立差分隐私，同时不丢失参与代理决策变量之间共享耦合约束的可能最优性;（4）在分布式Nash博弈中探索和建立差分隐私（本质上是非合作代理的分散优化问题），而不会失去可证明的最优性;（5）使用数值模拟以及在智能电网和联网智能车辆中的真实世界分布式系统上的实验来评估所获得的结果。该项目由计算和通信基金会（CCF）核心计划和激励竞争研究的既定计划（EPSCoR）共同资助。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。