Privacy-Preserving Shortest Path Computation
This addresses privacy concerns for users of navigation services, offering a fully-private alternative to existing systems that require revealing sensitive location data.
The paper tackles the problem of privacy in cloud-based navigation by developing a cryptographic protocol that protects both the client's location and the service provider's routing data, achieving over tenfold reduction in representation size for city maps like Los Angeles and demonstrating practicality on real street data for major cities.
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.