Differentially Private Trajectory Data Publication

TL;DR

This paper presents a practical, efficient method for publishing large-scale trajectory data with differential privacy, using a data-dependent sanitization approach that maintains high utility for analysis tasks.

Contribution

It introduces the first scalable, data-dependent differentially private sanitization algorithm for trajectory data, utilizing a noisy prefix tree and constrained inference for improved utility.

Findings

The approach achieves high utility in count queries and pattern mining.

It is scalable to large trajectory datasets.

Experiments on real data demonstrate effectiveness and efficiency.

Abstract

With the increasing prevalence of location-aware devices, trajectory data has been generated and collected in various application domains. Trajectory data carries rich information that is useful for many data analysis tasks. Yet, improper publishing and use of trajectory data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving trajectory data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing trajectory data under the rigorous differential privacy model. We propose an efficient data-dependent yet differentially private sanitization algorithm, which is applicable to different types of trajectory data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree to conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publishing large volume of trajectory data under differential privacy. We examine the utility of sanitized data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life trajectory data from the STM demonstrate that our approach maintains high utility and is scalable to large trajectory datasets.