Q-DP-GAN: Improving EEG Data Privacy Through Quantum-Inspired Differential Privacy-Based GAN

Electroencephalography (EEG)-based brain–computer interface (BCI) systems pose significant privacy risks, as EEG data remain vulnerable to inference and reconstruction attacks. Conventional privacy-preserving techniques, including data anonymization, encryption, and perturbation, frequently compromise data utility or prove ineffective against advanced adversaries. To address these limitations and balance utility and privacy, we propose a quantum-inspired, differential privacy-based generative adversarial network (Q-DP-GAN). Unlike classical GANs, which lack adaptive privacy mechanisms during training, our method uses quantum-inspired stochasticity to dynamically calibrate noise and the privacy budget. The experimental results demonstrate that Q-DP-GAN is more robust to membership inference and reconstruction attacks than existing approaches. Evaluation on the widely used BCI Competition IV Datasets 2A and 2B indicates that our framework produces high-quality synthetic EEG data while maintaining utility and data confidentiality for BCI classification tasks.