arXiv Machine Learning September 11, 2026 By Noman Sadiq, Mohsen Toorani

Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology

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The paper investigates how subject‑level differential privacy (DP) can anonymize EEG‑derived feature representations while preserving clinical utility. It evaluates Gaussian and Laplace perturbations across three deployment scenarios—client‑side, server‑side, and decentralized local training—using statistical utility metrics and a downstream machine‑learning task. Results indicate that DP can be integrated into EEG workflows, but the choice of mechanism, privacy parameters, and sensitivity calibration critically affects data utility, especially in small, imbalanced clinical datasets.

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