# Privacy-Aware Human Mobility Prediction via Adversarial Networks

arXiv: https://arxiv.org/abs/2201.07519 · HTML: https://mlsanity.com/paper/2201.07519/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2007.14189/) (2020)
- [Protecting Locations with Differential Privacy under Temporal Correlations](https://mlsanity.com/paper/1410.5919/) (2014)
- [Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)](https://mlsanity.com/paper/2004.04145/) (2020)
- [Privacy and Utility Preserving Sensor-Data Transformations](https://mlsanity.com/paper/1911.05996/) (2019)
- [Application-driven Privacy-preserving Data Publishing with Correlated Attributes](https://mlsanity.com/paper/1812.10193/) (2018)
- [Privacy Adversarial Network: Representation Learning for Mobile Data Privacy](https://mlsanity.com/paper/2006.06535/) (2020)

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

- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams](https://mlsanity.com/paper/2507.18072/) (2025)

## Abstract (arXiv)

As various mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. While these geolocated data could provide a rich understanding of human mobility patterns and address various societal research questions, privacy concerns for users' sensitive information have limited their utilization. In this paper, we design and implement a novel LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (mobility data) for a sharing purpose. We quantify the utility-privacy trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. Our proposed architecture reports a Pareto Frontier analysis that enables the user to assess this trade-off as a function of Lagrangian loss weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture and the efficiency of the proposed privacy-preserving features extractor. Our results show that by exploring Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).