Inherently privacy-preserving vision for trustworthy autonomous systems: Needs and solutions

Abstract

Vision is an effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, identity, and activity of humans within that scene. This raises important questions about the reach, lifespan, and misuse of this information. This paper is a call to action to consider privacy in robotic vision. We propose a specific form of inherent privacy preservation in which no images are captured or could be reconstructed by an attacker, even with full remote access. We present a set of principles by which such systems could be designed, employing data-destroying operations and obfuscation in the optical and analogue domains. These cameras never see a full scene. Our localisation case study demonstrates in simulation four implementations that all fulfil this task. The design space of such systems is vast despite the constraints of optical-analogue processing. We hope to inspire future works that expand the range of applications open to sighted robotic systems.

Keywords

• Privacy-preserving vision
• Optical computing
• Robotic imaging
• Localisation