Privacy-Aware Adversarial Network in Human Mobility Prediction

TL;DR

This paper introduces a novel LSTM-based adversarial mechanism that creates privacy-preserving representations of geolocated mobility data, effectively reducing re-identification risks while maintaining utility in smart city applications.

Contribution

It proposes a new adversarial learning framework for mobility data that balances privacy and utility, outperforming existing methods in privacy protection with minimal utility loss.

Findings

Achieves 45% privacy protection with 32% utility retention.

Demonstrates superiority over baseline privacy-preserving methods.

Enables adjustable privacy-utility trade-off via Pareto optimality.

Abstract

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%).