RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

TL;DR

RoboChain is a novel framework that enables secure, decentralized data and model sharing among robots in healthcare settings, leveraging blockchain and machine learning to preserve privacy during collaborative learning.

Contribution

It introduces RoboChain, the first framework combining blockchain and open data access for privacy-preserving, multi-site robot learning in clinical environments.

Findings

Demonstrates secure data sharing among hospital robots

Shows efficient collaborative learning without compromising privacy

Integrates blockchain with machine learning for healthcare robots

Abstract

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.