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Enhancing Privacy in Robotics via Judicious Sensor Selection

Stephen G. Eick, Annie I. Antón

Year
2020
Citations
17

Abstract

Roboticists are grappling with how to address privacy in robot design at a time when regulatory frameworks around the world increasingly require systems to be engineered to preserve and protect privacy. This paper surveys the top robotics journals and conferences over the past four decades to identify contributions with respect to privacy in robot design. Our survey revealed that less than half of one percent of the ~89,120 papers in our study even mention the word privacy. Herein, we propose privacy preserving approaches for roboticists to employ in robot design, including, assessing a robot's purpose and environment; ensuring privacy by design by selecting sensors that do not collect information that is not essential to the core objectives of that robot; embracing both privacy and performance as fundamental design challenges to be addressed early in the robot lifecycle; and performing privacy impact assessments.

Keywords

RobotRoboticsPrivacy by DesignComputer scienceInformation privacyArtificial intelligenceBehavior-based roboticsSelection (genetic algorithm)Core (optical fiber)Human–computer interaction

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