# LDPTrace: Locally Differentially Private Trajectory Synthesis

arXiv: https://arxiv.org/abs/2302.06180 · HTML: https://mlsanity.com/paper/2302.06180/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2023.

## Built on by (most cited first, 16 of 16)

- [DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model](https://mlsanity.com/paper/2304.11582/) (2023)
- [ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model](https://mlsanity.com/paper/2404.15380/) (2024)
- [FACE-AUDITOR: Data Auditing in Facial Recognition Systems](https://mlsanity.com/paper/2304.02782/) (2023)
- [DPMLBench: Holistic Evaluation of Differentially Private Machine Learning](https://mlsanity.com/paper/2305.05900/) (2023)
- [ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03081/) (2023)
- [Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus](https://mlsanity.com/paper/2409.13790/) (2024)
- [DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training](https://mlsanity.com/paper/2403.02571/) (2024)
- [Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion](https://mlsanity.com/paper/2507.13366/) (2025)
- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [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)
- [Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics](https://mlsanity.com/paper/2506.12203/) (2025)
- [Traj-Transformer: Diffusion Models with Transformer for GPS Trajectory Generation](https://mlsanity.com/paper/2510.06291/) (2025)
- [Efficient Prompt Learning for Traffic Forecasting](https://mlsanity.com/paper/2605.08273/) (2026)
- [DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2605.17432/) (2026)

## Abstract (arXiv)

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privay concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.