Shortest Path Computation with No Information Leakage

TL;DR

This paper introduces a privacy-preserving method for shortest path queries in location-based services that ensures no information leakage about the user's source or destination, using private information retrieval techniques.

Contribution

It presents a novel approach that guarantees zero information leakage in shortest path computations, surpassing existing obfuscation-based methods.

Findings

The proposed scheme achieves strong privacy with no information leakage.

Experiments demonstrate the practicality of the approach on large-scale road networks.

The method effectively prevents the LBS from inferring any details about the query.

Abstract

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.