Collaborative Research: EAGER: Foundations of Secure Multi-Robot Computation

协作研究：EAGER：安全多机器人计算的基础

• 批准号: 2034097
• 负责人: Dylan Shell
• 金额: $ 9.75万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034097&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Foundations; Secure

As people are starting to face the prospect of robots becoming part of our everyday lives, it has become increasingly clear that the information robots could gather can be both sensitive and valuable. But the robots may need to gather this information in order to function properly: as elsewhere in our lives, we need to understand how to best reconcile the tension between utility and privacy. The scientific progress made to date on algorithms for planning, control, and coordination of multi-robot systems has been enormous, but it also has paid too little attention to "who knows what." This research effort sets out to understand how the essential computational operations underlying many common robotic tasks can be safely accomplished in circumstances where there is some doubt about the integrity of other elements in the system, including whether they can be trusted to never expose information. This is crucial for autonomous robots operating within socially sensitive settings, as well as contested or adversarial scenarios. Beyond the anticipated impact on robotics research, the project will benefit society by addressing questions of strategic national interest and help facilitate privacy protections. It includes education and outreach activities that serve underrepresented groups, firstly via direct engagement with undergraduate and graduate students at Florida International University and Texas A&M, and secondly, in working with and mentoring high school teachers. The project will conduct both theoretical and empirical research, through a multi-part research agenda that will enable privacy-preserving filtering and planning in multi-robot scenarios via secure multi-party computation methods. This research endeavor represents a radical departure from present computational assumptions for robots: it aims to introduce abstractions, algorithms, and systems to solve robot tasks in scenarios characterized by collaboration between mutually distrusting robots, this is the first systematic effort to do so. The research will allow multiple robots to coordinate their use of shared resources, without divulging sensitive information that each robot possesses, despite their effective cooperation actually depending on that sensitive information. The research program will produce: (1) a new set of geometric primitives that allow the solution of motion planning (and related) problems in a privacy-preserving fashion; (2) novel filters, constructed to preserve privacy; and (3) constructions for calculating and querying computational topology properties, subject to limits on information shared. Together, these pieces lay groundwork, establishing the research area of secure multi-robot computation.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.

随着人们开始面对机器人成为我们日常生活的一部分的前景，越来越清楚的是，信息机器人可以收集的既敏感又有价值。 但是机器人可能需要收集此信息以正常运行：与我们生活中的其他地方一样，我们需要了解如何最好地调和效用与隐私之间的紧张局势。迄今为止，在计划，控制和协调多机器人系统的算法上取得的科学进步非常巨大，但它也很少关注“谁知道什么”。这项研究工作旨在了解如何在系统中其他元素的完整性（包括是否可以信任地可以永远不会暴露信息）的情况下安全地完成许多常见的机器人任务的基本计算操作。这对于在社会敏感的环境以及有争议或对抗性场景中运行的自动机器人至关重要。除了对机器人研究的预期影响之外，该项目还将通过解决战略国家利益问题并帮助促进隐私保护来使社会受益。它包括为代表性不足的团体提供服务的教育和外展活动，首先是与佛罗里达国际大学和德克萨斯A＆M的本科生和研究生直接互动，其次是与高中老师合作和指导。该项目将通过多部分研究议程进行理论和实证研究，该议程将通过安全的多方计算方法在多机器人方案中实现隐私性过滤和计划。这项研究的努力代表了机器人目前的计算假设的根本性：它旨在引入抽象，算法和系统，以在相互不信任机器人之间协作的情况下解决机器人任务，这是第一个系统的努力。这项研究将允许多个机器人协调他们对共享资源的使用，而无需泄露每个机器人所拥有的敏感信息，尽管实际上它们取决于该敏感信息。该研究计划将产生：（1）一组新的几何原始素，以保护隐私的方式解决运动计划（及相关）问题的解决方案； （2）旨在保护隐私的新颖过滤器； （3）用于计算和查询计算拓扑属性的构造，但要受共享信息的限制。这些作品共同建立了基础，建立了安全多机器人计算的研究领域。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

项目成果

A general class of combinatorial filters that can be minimized efficiently

一类可以有效最小化的通用组合滤波器

• DOI: 10.1109/icra48891.2023.10160479
• 发表时间: 2023
• 期刊: IEEE
• 作者: Zhang, Yulin;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

On nondeterminism in combinatorial filters

组合滤波器中的非确定性

• DOI: 10.1109/icra46639.2022.9812371
• 发表时间: 2022
• 期刊: IEEE
• 作者: Zhang, Yulin;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Privacy-Preserving Multi-Robot Task Allocation via Secure Multi-Party Computation

• DOI: 10.23919/ecc55457.2022.9838150
• 发表时间: 2022-01-01
• 期刊: 2022 EUROPEAN CONTROL CONFERENCE (ECC)
• 作者: Alsayegh, Murtadha;Vanegas, Peter;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Planning under periodic observations: bounds and bounding-based solutions

定期观察下的规划：边界和基于边界的解决方案

• DOI: 10.1109/iros47612.2022.9982056
• 发表时间: 2022
• 期刊: IEEE
• 作者: Rossi, Federico;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Sensor Selection for Fine-Grained Behavior Verification that Respects Privacy

• DOI: 10.1109/iros55552.2023.10341877
• 发表时间: 2023-10
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Rishi Phatak;Dylan A. Shell