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

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.