Localization is a computer vision task by which the position and orientation of a camera is determined from an image and environmental map. We propose a method for performing localization in a privacy preserving manner supporting two scenarios: first, when the image and map are held by a client who wishes to offload localization to untrusted third parties, and second, when the image and map are held separately by untrusting parties. Privacy preserving localization is necessary when the image and map are confidential, and offloading conserves on-device power and frees resources for other tasks. To accomplish this we integrate existing localization methods and secure multi-party computation (MPC), specifically garbled circuits, yielding proof-based security guarantees in contrast to existing obfuscation-based approaches which recent related work has shown vulnerable. We present two approaches to localization, a baseline data-oblivious adaptation of localization suitable for garbled circuits and our novel Single Iteration Localization. Our technique improves overall performance while maintaining confidentiality of the input image, map, and output pose at the expense of increased communication rounds but reduced computation and communication required per round. Single Iteration Localization is over two orders of magnitude faster than a straightforward application of garbled circuits to localization enabling real-world usage in the first robot to offload localization without revealing input images, environmental map, position, or orientation to offload servers.

翻译：定位是一种计算机视觉任务，通过图像和环境地图确定相机的位置与朝向。我们提出了一种隐私保护下的定位方法，支持两种场景：其一，图像与地图由客户端持有，客户端希望将定位任务外包给不可信的第三方；其二，图像与地图分别由互不信任的双方持有。当图像与地图属于机密时，隐私保护定位至关重要，而任务外包则可节省设备端电量并释放资源用于其他任务。为实现这一目标，我们整合了现有定位方法与安全多方计算（MPC），特别是混淆电路，从而提供基于证明的安全性保证——这与现有基于混淆的方法（近期相关工作已证明其存在漏洞）形成对比。我们提出了两种定位方法：一种适用于混淆电路的基线数据无关定位方案，以及我们创新提出的单次迭代定位。该技术以增加通信轮次为代价，减少了每轮所需的计算与通信量，从而在保持输入图像、地图及输出位姿机密性的同时提升整体性能。与直接将混淆电路应用于定位的朴素方案相比，单次迭代定位的速度提升了两个数量级以上，使得首个在不向外包服务器泄露输入图像、环境地图、位置或朝向的情况下外包定位任务的机器人得以实现实际应用。