# SoK: Can Trajectory Generation Combine Privacy and Utility?

arXiv: https://arxiv.org/abs/2403.07218 · HTML: https://mlsanity.com/paper/2403.07218/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [An Overview of Privacy in Machine Learning](https://mlsanity.com/paper/2005.08679/) (2020)
- [Continuous Trajectory Generation Based on Two-Stage GAN](https://mlsanity.com/paper/2301.07103/) (2023)
- [The Long Road to Computational Location Privacy: A Survey](https://mlsanity.com/paper/1810.03568/) (2018)
- [GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy](https://mlsanity.com/paper/2205.08886/) (2022)
- [Real-World Trajectory Sharing with Local Differential Privacy](https://mlsanity.com/paper/2108.02084/) (2021)
- [Privacy-Preserving Synthetic Location Data in the Real World](https://mlsanity.com/paper/2108.02089/) (2021)
- [Reconstruction Attack on Differential Private Trajectory Protection Mechanisms](https://mlsanity.com/paper/2210.09375/) (2022)
- [A Predictive Differentially-Private Mechanism for Mobility Traces](https://mlsanity.com/paper/1311.4008/) (2013)

## Built on by (most cited first, 6 of 6)

- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Synthetic Trajectory Generation Through Convolutional Neural Networks](https://mlsanity.com/paper/2407.16938/) (2024)
- [Demystifying Trajectory Recovery From Ash: An Open-Source Evaluation and Enhancement](https://mlsanity.com/paper/2409.14645/) (2024)
- [What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?](https://mlsanity.com/paper/2506.09312/) (2025)
- [diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories](https://mlsanity.com/paper/2605.10647/) (2026)
- [Privacy Evaluation of Generative Models for Trajectory Generation](https://mlsanity.com/paper/2605.15246/) (2026)

## Abstract (arXiv)

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.