M. A.

Quantum-inspired scenario-adaptive differential privacy with hybrid attention–residual deep learning for utility-aware urban location analytics

A Quantum-Inspired Scenario-Adaptive Differential Privacy framework that combines k-nearest-neighbor (kNN)-based adaptive privacy budgeting with structured quantum-inspired noise generation and a Hybrid Attention–Residual Deep Neural Network for mechanism selection demonstrates that structured quantum-inspired perturbations can substantially improve privacy–utility trade-offs while maintaining robustness across independent real-world mobility datasets.