# Where you go is who you are -- A study on machine learning based semantic privacy attacks

arXiv: https://arxiv.org/abs/2310.17643 · HTML: https://mlsanity.com/paper/2310.17643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 87 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells](https://mlsanity.com/paper/2003.00824/) (2020)
- [Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data](https://mlsanity.com/paper/1501.06814/) (2015)
- [Elastic Pathing: Your Speed is Enough to Track You](https://mlsanity.com/paper/1401.0052/) (2013)
- [Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility](https://mlsanity.com/paper/2112.08487/) (2021)

## Built on by (most cited first, 1 of 1)

- [Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy](https://mlsanity.com/paper/2606.27936/) (2026)

## Abstract (arXiv)

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.