Privacy-Preserving Shortest Path Computation

TL;DR

This paper introduces a cryptographic protocol for privacy-preserving navigation that protects user location and routing data, utilizing a novel compression method for routing matrices to enable efficient real-time city navigation.

Contribution

It presents a new cryptographic protocol combined with a matrix compression technique that significantly reduces data size for private navigation on city streets.

Findings

Over tenfold reduction in routing matrix size for Los Angeles

Efficient protocol demonstrated on real city maps

Practical real-time privacy-preserving navigation achieved

Abstract

Navigation is one of the most popular cloud computing services. But in virtually all cloud-based navigation systems, the client must reveal her location and destination to the cloud service provider in order to learn the fastest route. In this work, we present a cryptographic protocol for navigation on city streets that provides privacy for both the client's location and the service provider's routing data. Our key ingredient is a novel method for compressing the next-hop routing matrices in networks such as city street maps. Applying our compression method to the map of Los Angeles, for example, we achieve over tenfold reduction in the representation size. In conjunction with other cryptographic techniques, this compressed representation results in an efficient protocol suitable for fully-private real-time navigation on city streets. We demonstrate the practicality of our protocol by benchmarking it on real street map data for major cities such as San Francisco and Washington, D.C.