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大型分布式数据采集系统网络得以激增，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。与这些进展相辅相成的是分散优化软件方面的重大进展，该软件使这种分布式联网系统的基本功能得以实现，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，在如此大型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车载网络等隐私敏感应用程序中可能尤为严重。差异隐私是使用最广泛的隐私保护机制，因为它简单、可扩展，并且对从后处理数据中恢复敏感信息的尝试具有很强的弹性。然而，所有现有的用于分散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精度来实现数据隐私保护的两难境地。这个项目利用了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不同的隐私保证。具体地说，该项目将为在不损失可证明的最优性的情况下确保分散优化中的差异隐私的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目前关于控制和网络系统的课程，以及培训本科生和研究生进行跨学科的信息隐私研究及其应用，来影响教育。该项目将为在不损失可证明的最优性的情况下确保去中心化优化算法中的差异隐私建立理论和算法基础。主要研究内容是：(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在收敛速度上的牺牲；(2)探索并建立在不丢失可证明最优性的分散在线优化中的差分隐私；(3)在具有共享耦合约束的分散优化算法中探索并建立差分隐私而不丢失概率最优性；(4)在不损失可证明最优性的分散Nash博弈(本质上是具有非合作代理的分散优化问题)中探索并建立差分隐私；(5)在智能电网和网络化智能汽车的实际分布式系统上进行了数值模拟和实验，对所得结果进行了评估。该项目由计算和通信基础部门(CCF)的核心计划和既定的激励竞争研究计划(EPSCoR)联合资助。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。

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.