# Privacy in trajectory micro-data publishing : a survey

arXiv: https://arxiv.org/abs/1903.12211 · HTML: https://mlsanity.com/paper/1903.12211/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)
- [CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches](https://mlsanity.com/paper/2309.11587/) (2023)
- [Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data](https://mlsanity.com/paper/2308.07940/) (2023)
- [From Narrative to Action: A Hierarchical LLM-Agent Framework for Human Mobility Generation](https://mlsanity.com/paper/2510.24802/) (2025)

## Abstract (arXiv)

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.