# Privacy risk in GeoData: A survey

arXiv: https://arxiv.org/abs/2402.03612 · HTML: https://mlsanity.com/paper/2402.03612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)

## Abstract (arXiv)

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The widespread exposure of such location data poses significant privacy risks to users, as it can lead to re-identification, the inference of sensitive information, and even physical threats. In this survey, we analyse different geomasking techniques proposed to protect individuals' privacy in geodata. We propose a taxonomy to characterise these techniques across various dimensions. We then highlight the shortcomings of current techniques and discuss avenues for future research. Our proposed taxonomy serves as a practical resource for data custodians, offering them a means to navigate the extensive array of existing privacy mechanisms and to identify those that align most effectively with their specific requirements.