# Privacy-Aware Adversarial Network in Human Mobility Prediction

arXiv: https://arxiv.org/abs/2208.05009 · HTML: https://mlsanity.com/paper/2208.05009/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 65 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [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)
- [Context-Aware Generative Adversarial Privacy](https://mlsanity.com/paper/1710.09549/) (2017)
- [Protecting Locations with Differential Privacy under Temporal Correlations](https://mlsanity.com/paper/1410.5919/) (2014)
- [Minimax Filter: Learning to Preserve Privacy from Inference Attacks](https://mlsanity.com/paper/1610.03577/) (2016)
- [Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02685/) (2020)
- [The Long Road to Computational Location Privacy: A Survey](https://mlsanity.com/paper/1810.03568/) (2018)
- [Privacy and Utility Preserving Sensor-Data Transformations](https://mlsanity.com/paper/1911.05996/) (2019)
- [Privacy-Preserving Synthetic Location Data in the Real World](https://mlsanity.com/paper/2108.02089/) (2021)
- [Differentially Private Location Privacy in Practice](https://mlsanity.com/paper/1410.7744/) (2014)
- [Utility-Optimized Synthesis of Differentially Private Location Traces](https://mlsanity.com/paper/2009.06505/) (2020)

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

- [Analysing Fairness of Privacy-Utility Mobility Models](https://mlsanity.com/paper/2304.06469/) (2023)
- [Private Vertical Federated Inference for Time-Series](https://mlsanity.com/paper/2605.08343/) (2026)

## Abstract (arXiv)

As 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. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).