# Real-World Trajectory Sharing with Local Differential Privacy

arXiv: https://arxiv.org/abs/2108.02084 · HTML: https://mlsanity.com/paper/2108.02084/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2021.

## Built on by (most cited first, 8 of 8)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy](https://mlsanity.com/paper/2205.08886/) (2022)
- [A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management](https://mlsanity.com/paper/2306.02806/) (2023)
- [SoK: Can Trajectory Generation Combine Privacy and Utility?](https://mlsanity.com/paper/2403.07218/) (2024)
- [Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)](https://mlsanity.com/paper/2603.12142/) (2026)
- [Data Poisoning Attacks to Locally Differentially Private Range Query Protocols](https://mlsanity.com/paper/2503.03454/) (2025)
- [Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data](https://mlsanity.com/paper/2503.07483/) (2025)
- [Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis](https://mlsanity.com/paper/2411.01929/) (2024)

## Abstract (arXiv)

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.