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.
”Deep learning for person re-identificat ion: A survey and outlook.” IEEE transactions on pattern analysis and machin e intelligence 44.6 (2021): 2872-2893
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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.