CIF: Small: Ensuring Accuracy in Differentially Private Decentralized Optimization

无线通信和低成本计算设备的进步使数据收集系统的大型分布式网络得以扩散，构成了新兴的物联网（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分散优化软件的重大进展，它使这种分布式网络系统的基本功能成为可能，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，在如此庞大的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汽车网络等对隐私敏感的应用中尤其严重。差分隐私是最广泛使用的隐私保护机制，因为它简单、可扩展性和强大的弹性，可以防止从后处理数据中恢复敏感信息。然而，所有现有的分散优化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，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异隐私保证。具体来说，该项目将为在不失去可证明的最优性的情况下确保分散优化中的差异隐私问题建立理论和算法基础。除了广泛地为分散的网络提供更有效的隐私保护外，该项目还将通过丰富当前关于控制和网络系统的课程，以及培训本科生和研究生跨学科信息隐私研究及其应用，来影响教育。该项目将为确保分散优化算法中的差异隐私而不失去可证明的最优性奠定理论和算法基础。主要研究方向是：(1)利用信息论方法研究具有可证明最优性的差分私有分散优化的收敛速度牺牲；(2)在分散式在线优化中，探索并建立不丢失可证明最优性的差异隐私，在实现算法之前不预先收集数据，而是以顺序方式获取数据；(3)在参与主体决策变量之间存在共享耦合约束的分散优化算法中，探索并建立不损失可能最优性的差异隐私；(4)在不失去可证明最优性的情况下，探索和建立分散纳什博弈（本质上是具有非合作代理的分散优化问题）中的差分隐私；(5)利用数值模拟以及在智能电网和联网智能车辆的实时分布式系统上的实验来评估获得的结果。本项目由计算和通信基础部（CCF）的核心项目和促进竞争研究的既定项目（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

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.