Demo: Privacy-Preserving Localization for Edge-Assisted Robotic Navigation

James Choncholas, Pujith Kachana, André Mateus, Gregoire Phillips, Ada Gavrilovska

Abstract: Localization is a common task in computer vision by which the position and orientation of a camera is determined from an image and 3D map of the environment. We propose a demonstration of securely performing localization in a privacy preserving manner. This allows localization so that a lightweight client device, such as a mobile robot, may offload the computation to an untrusted server without revealing anything about their data. To accomplish this goal, we combine existing localization methods with secure multi-party computation (MPC), specifically garbled circuits. As such, the security guarantees of this work are simulation-based in contrast to existing obfuscation-based approaches to pose estimation for which privacy is inversely proportional to input size. We propose two approaches, a baseline data-oblivious adaptation of localization suitable for MPC and Single Iteration Localization which runs each localization step individually, improving performance at the cost of round complexity while maintaining confidentiality of the input image, map, and output pose. Single Iteration Localization is over two orders of magnitude faster than the data-oblivious approach enabling real-world usage in Turbo the Snail, the first robot to offload localization without revealing input images, environmental map, position, or orientation to offload servers.