A framework using shuffling and Laplace noise claims to balance privacy, utility, and fairness for vehicular traffic data, but the guarantees reduce to definitions and experiments use synthetic data.
”The year in risk 2017.” Risk Manageme nt 64.11 (2017): 20-25
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Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System
A framework using shuffling and Laplace noise claims to balance privacy, utility, and fairness for vehicular traffic data, but the guarantees reduce to definitions and experiments use synthetic data.