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

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.
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.

Dates Active: 2020 — 2022