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Analysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories.
Privacy-Aware Adversarial Network in Human Mobility Prediction
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.
Privacy-Aware Human Mobility Prediction via Adversarial Networks
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.
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