Private Location Sharing for Decentralized Routing services
Matthew Tsao, Kaidi Yang, Karthik Gopalakrishnan, Marco Pavone

TL;DR
This paper introduces a decentralized, privacy-preserving protocol for location sharing in routing services that protects user data using cryptography and differential privacy, maintaining accuracy and efficiency.
Contribution
It presents a novel protocol combining secure multi-party computation and the Laplace mechanism to ensure privacy in location-based routing without central data collection.
Findings
Travel time estimates are close to ground truth for high-capacity roads
The protocol provides strong privacy guarantees with minimal impact on system performance
Numerical experiments confirm the protocol's efficiency and privacy preservation
Abstract
Data-driven methodologies offer many exciting upsides, but they also introduce new challenges, particularly in the realm of user privacy. Specifically, the way data is collected can pose privacy risks to end users. In many routing services, a single entity (e.g., the routing service provider) collects and manages user trajectory data. When it comes to user privacy, these systems have a central point of failure since users have to trust that this entity will not sell or use their data to infer sensitive private information. Unfortunately, in practice many advertising companies offer to buy such data for the sake of targeted advertisements. With this as motivation, we study the problem of using location data for routing services in a privacy-preserving way. Rather than having users report their location to a central operator, we present a protocol in which users participate in a…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Traffic control and management · Traffic Prediction and Management Techniques
