Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis

TL;DR

This paper introduces a hybrid differential privacy method for processing multimodal vehicular traffic data that balances privacy and accuracy, demonstrated through real-world experiments.

Contribution

It presents a novel hybrid differential privacy approach tailored for vehicular traffic data, improving privacy preservation while maintaining high accuracy.

Findings

Significantly outperforms baseline methods in accuracy

Effectively preserves individual vehicle privacy

Validated with real-world traffic data

Abstract

We estimate vehicular traffic states from multimodal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To this end, we propose a novel hybrid differential privacy (DP) approach that utilizes minimal randomization to preserve privacy by taking advantage of the relevant traffic state dynamics and the concept of DP sensitivity. Through theoretical analysis and experiments with real-world data, we show that the proposed approach significantly outperforms the related baseline non-private and private approaches in terms of accuracy and privacy preservation.