Real-World Trajectory Sharing with Local Differential Privacy

TL;DR

This paper introduces a local differential privacy mechanism for trajectory sharing that leverages publicly available external knowledge to produce more realistic and useful shared trajectories without compromising privacy.

Contribution

It proposes a novel hierarchical perturbation method that incorporates public data into local differential privacy for trajectory sharing, enhancing utility and realism.

Findings

Outperforms existing methods in utility and realism.

Maintains privacy and efficiency despite using public data.

Effective on real-world trajectory datasets.

Abstract

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping $n$-grams (i.e., contiguous subsequences of length $n$) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.