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Collaborative Research: CIF:Medium: Harnessing Intrinsic Dynamics for Inherently Privacy-preserving Decentralized Optimization

合作研究：CIF：Medium：利用内在动力学实现固有隐私保护的去中心化优化

基本信息

批准号：
2106336
负责人：
Angelia Nedich
金额：
$ 49.99万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106336&HistoricalAwards=false
关键词：
Collaborative Research CIF Medium Harnessing

项目摘要

Recent advances in communication and networking technologies lead to the emergence and proliferation of distributed interconnected systems such as swarm robotics, sensor networks, smart-grid, the Internet of Things, and collaborative machine-learning systems. A task that is fundamental to the operation of these systems is decentralized optimization, where participating nodes cooperate to minimize an overall objective function that is the sum (or average) of individual nodes’ local objective functions. Moreover, since individual nodes’ local objective functions may bear sensitive information of local nodes such as medical records in collaborative learning and user energy-consumption profiles in a smart grid, in many cases, the decentralized optimization algorithm has to make sure that a participant’s sensitive information is protected from being inferable by other participating nodes or external eavesdroppers. Although plenty of results have been proposed for decentralized optimization, most of these results do not consider the problem of privacy protection. Conventional information-technology privacy mechanisms are inappropriate for decentralized optimization because they either have to compromise the accuracy of optimization (in, e.g., differential-privacy-based approaches) or incur heavy extra computation/communication overhead (in, e.g., cryptography-based privacy approaches). The lack of effective privacy solutions for decentralized optimization not only severely hinders the social adoption of new technologies, but also leads to potential vulnerabilities since stealing private information is usually the basis for sophisticated cybersecurity attacks. Leveraging the iterative properties of decentralized optimization algorithms, the project aims to establish a new privacy-preserving approach for decentralized optimization that neither compromises optimization accuracy nor incurs large computation/communication overhead. Combined with the additional merit of needing no assistance of a trusted central coordinator, the proposed approach is expected to transformatively advance privacy-preservation in networked systems and make impacts in many applications ranging from connected vehicles, swarm robotics, smart grid, sensor networks, to collaborative machine learning. Leveraging control theory, this project seeks to establish methodologies and associated theories for inherently privacy-preserving decentralized optimization by exploiting the intrinsic dynamical properties of decentralized optimization. Besides maintaining optimization accuracy, the dynamics-based privacy approach is also free of encryption, which not only guarantees limited extra computation/communication overhead, but also promises a decentralized implementation without the assistance of any trusted third party or data aggregator. The main research thrusts are to: 1) Develop a privacy framework for dynamical systems that explicitly considers the iterative evolution of information in decentralized optimization; 2) Design perturbations to dynamics that enable privacy without affecting the accuracy of decentralized optimization methods for convex problems, and quantify the effects of the perturbations on convergence speed; 3) Investigate the influence of privacy design on decentralized non-convex optimization and exploit freedom in privacy design to facilitate decentralized non-convex problems; and 4) Evaluate the results using experiments on a multi-robot platform and connected vehicles.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.

通信和网络技术的最新进展导致分布式互联系统的出现和扩散，如群体机器人、传感器网络、智能电网、物联网和协作机器学习系统。分散优化是这些系统运行的基本任务，其中参与节点合作最小化总体目标函数，即单个节点局部目标函数的总和（或平均值）。此外，由于单个节点的局部目标函数可能包含局部节点的敏感信息，如协同学习中的医疗记录和智能电网中的用户能耗曲线，在许多情况下，分散优化算法必须确保参与者的敏感信息不被其他参与节点或外部窃听者推断。虽然已经提出了大量的去中心化优化结果，但这些结果大多没有考虑隐私保护问题。传统的信息技术隐私机制不适合去中心化优化，因为它们要么会损害优化的准确性（例如，基于差分隐私的方法），要么会产生大量额外的计算/通信开销（例如，基于加密的隐私方法）。缺乏有效的分散优化隐私解决方案不仅严重阻碍了新技术的社会采用，而且还导致潜在的漏洞，因为窃取私人信息通常是复杂网络安全攻击的基础。利用去中心化优化算法的迭代特性，该项目旨在为去中心化优化建立一种新的隐私保护方法，既不会损害优化精度，也不会产生大量的计算/通信开销。结合不需要可信中央协调器帮助的额外优点，所提出的方法有望在网络系统中彻底推进隐私保护，并在许多应用中产生影响，从联网车辆、群体机器人、智能电网、传感器网络到协作机器学习。利用控制理论，该项目旨在通过利用分散优化的内在动态特性，建立固有隐私保护分散优化的方法和相关理论。除了保持优化的准确性外，基于动态的隐私方法也没有加密，这不仅保证了有限的额外计算/通信开销，而且还承诺在没有任何可信第三方或数据聚合器帮助的情况下实现分散。主要研究方向是：1)建立一个明确考虑分散优化中信息迭代演化的动态系统隐私框架；2)设计对动力学的扰动，使隐私不影响分散优化方法对凸问题的准确性，并量化扰动对收敛速度的影响；3)研究隐私设计对去中心化非凸优化的影响，利用隐私设计中的自由度来解决去中心化非凸问题；4)在多机器人平台和网联车辆上对实验结果进行评估。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。