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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连系统的出现和扩散，例如群机器人、传感器网络、智能电网、物联网和协作机器学习系统。对这些系统的操作来说基本的任务是分散式优化，其中参与节点合作以最小化总体目标函数，该总体目标函数是各个节点的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承载局部节点的敏感信息，例如协作学习中的医疗记录和智能电网中的用户能耗简档，因此在许多情况下，分散式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。虽然已经有大量的分散优化结果被提出，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分散式优化，因为它们要么必须损害优化的准确性（例如，基于差异隐私的方法）或招致沉重的额外计算/通信开销（例如，基于加密的隐私方法）。缺乏有效的去中心化优化隐私解决方案不仅严重阻碍了社会对新技术的采用，而且还导致了潜在的漏洞，因为窃取私人信息通常是复杂的网络安全攻击的基础。利用分散优化算法的迭代特性，该项目旨在为分散优化建立一种新的隐私保护方法，既不会影响优化精度，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的帮助的额外优点，预计所提出的方法将变革性地推进网络系统中的隐私保护，并在许多应用中产生影响，从联网车辆，群机器人，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力学特性，建立内在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，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，而且还承诺了一个分散的实现，而无需任何可信的第三方或数据聚合器的帮助。主要研究方向是：1）建立一个动态系统的隐私框架，明确考虑分散优化中信息的迭代演化; 2）设计动态扰动，使隐私不影响凸问题分散优化方法的精度，并量化扰动对收敛速度的影响; 3）研究隐私设计对分散非凸优化的影响，利用隐私设计中的自由度来简化分散非凸优化问题;和4）使用多个实验来评估结果，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。