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 Turbo the Snail, the first robot to offload localization without revealing input images, environmental map, position, or orientation to offload servers.

Demand for distributed computational infrastructure is growing in order to offer low latency connections to end users. The fragmenting infrastructure complicates the resource allocation process. As the number of infrastructure providers grows, points of presence are resource constrained compared to the cloud, they have diverse availability profiles, and diverse connectivity properties. Existing resource allocation approaches require providers share intimate details about their infrastructure to support the placement process, or rely on third party aggregators. Such solutions introduce strong assumptions of trust and collaboration. In this work we present Angler, the first system to allocate resources from dark pools, meaning the capacity and requests of the distributed pool of resources are unknown. Angler leverages cryptographic protocols for secure function evaluation, namely the WRK secure multiparty computation (MPC) protocol [76]. While MPC protocols can have large overheads compared to plaintext function evaluation, an end-to-end approach to the system design subverts the expensive overheads. Specifically, Angler combines a tuned implementation of a maliciously secure MPC protocol, a tailored distributed hash table, and a systematic effort to make the best allocation decision within a response time envelope. Angler is only 2x slower than resource allocation with no privacy when arbitrating among 8 providers, taking less than a second.

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 multiparty 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.

Mobile edge computing (MEC) can transform mobile networks into a new infrastructure tier for services requiring low response times, such as those providing content to emerging AR/VR, autonomous driving, and other types of applications. To be successful, the CDNs operating in this MEC infrastructure tier MEC-CDNs will need to ensure end user applications gain access to a cache server in a fast and accurate manner. This paper sheds light on the challenges that the current mobile DNS architecture poses toward achieving this goal, and presents ideas on how to re-architect the existing DNS architecture to enable CDNs to provide low-latency content delivery from the edge.