Protecting User Data Through Privacy-Sensitive Robot Design

Abstract

While robots possess many capabilities that may positively influence human lives, their autonomous navigation and sensing capabilities pose threats to user privacy. These threats may be addressed at three key phases: data collection, data retention, and data exposure. In this work, we discuss our prior, current, and proposed robot design efforts to reduce privacy violations during human-robot interaction (HRI). At the data collection phase, we are currently exploring designs that enable robots to inhibit data collection by blocking their own sensors. At the data retention phase, we propose the exploration of privacy preferences to inform designs that grant users greater control over retained data. Finally, in the data exposure phase, we discuss our prior works developing a privacy controller for appropriate data exposure and generating task-planning strategies to limit unintentional data exposure. Through this work, we hope to protect user data and reduce the likelihood of harm to users.