Privacy-preserving Travel Time Prediction with Uncertainty Using GPS Trace Data

TL;DR

This paper introduces a privacy-preserving method for travel time prediction using GPS data, leveraging geo-indistinguishability to protect individual privacy while maintaining prediction accuracy.

Contribution

It proposes a novel privacy-preserving framework for travel time prediction that does not require collecting raw GPS traces, utilizing geo-indistinguishability and new utility metrics.

Findings

Achieves satisfactory travel time prediction accuracy with strong privacy guarantees.

Provides analytical and experimental utility analysis for privacy-preserving GPS data.

Introduces new metrics to evaluate adversary success in reconstructing GPS traces.

Abstract

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.