Privacy-Preserving Robotic Perception for Object Detection in Curious Cloud Robotics

Abstract

Cloud robotics allows low-power robots to perform computationally intensive inference tasks by offloading them to the cloud, raising privacy concerns when transmitting sensitive images. Although end-to-end encryption secures data in transit, it does not prevent misuse by inquisitive third-party services since data must be decrypted for processing. This paper tackles these privacy issues in cloud-based object detection tasks for service robots. We propose a co-trained encoder-decoder architecture that retains only task-specific features while obfuscating sensitive information, utilizing a novel weak loss mechanism with proposal selection for privacy preservation. A theoretical analysis of the problem is provided, along with an evaluation of the trade-off between detection accuracy and privacy preservation through extensive experiments on public datasets and a real robot.