# Real-World Trajectory Sharing with Local Differential Privacy

Source: [Real-World Trajectory Sharing with Local Differential Privacy](<https://www.sota2.com/research/paper/68fa6a951e209131cea290ba>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:28:26.804Z

Authors: Teddy Cunningham, Graham Cormode, Hakan Ferhatosmanoglu, Divesh Srivastava

Published: 2021-08-04T14:37:01.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Trajectory Perturbation on TKY (first 100 trajectories)](<https://www.sota2.com/research/sota/trajectory-perturbation-on-tky-first-100-trajectories.md>): Trajectory Perturbation; TKY (first 100 trajectories); Total Time Cost (ms): 100.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.02084>)

[PDF](<https://arxiv.org/pdf/2108.02084.pdf>)