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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint describes a framework for applying differential privacy (Gaussian and Laplace perturbations) to anonymize clinical EEG-derived features across three deployment scenarios. The work demonstrates technical feasibility but identifies substantial privacy-utility trade-offs and challenges in preserving downstream machine-learning utility, particularly in small and imbalanced datasets; clinical validation and peer review are absent.
Methodological framework development and computational utility evaluation. Clinical EEG datasets; specific patient cohort, sample size, and setting not stated in abstract.. Intervention: Subject-level differential privacy applied to EEG-derived feature representations using Gaussian and Laplace perturbations across three deployment scenarios: client-side, centralized server-side, and decentralized local training..
Differentially private perturbation can be integrated into EEG processing workflows Selected privacy mechanisms, parameters, and sensitivity calibration strongly influence data utility Challenges identified in preserving downstream utility in small and imbalanced clinical EEG datasets
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This work is methodological and addresses technical governance (privacy protection) rather than clinical efficacy. Clinicians and researchers should recognize it as early-stage exploration of a privacy framework; it does not yet demonstrate whether differentially private EEG features remain fit for diagnostic or prognostic use.
This is a methodological proof-of-concept study of differential privacy applied to EEG features, demonstrating feasibility and trade-offs but without clinical validation, patient outcomes, or peer review.
As stated by the source record.
This work is methodological and addresses technical governance (privacy protection) rather than clinical efficacy. Clinicians and researchers should recognize it as early-stage exploration of a privacy framework; it does not yet demonstrate whether differentially private EEG features remain fit for diagnostic or prognostic use.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decision-support systems, but EEG recordings and derived features may contain sensitive patient-specific information. This creates privacy risks when data are reused, analyzed, or shared across clinical and research environments. Conventional anonymization methods are often insufficient for high-dimensional biomedical signals, since removing direct identifiers does not necessarily prevent re-identification, linkage, or inference risks. At the same time, strong privacy protection may distort clinically relevant signal characteristics and reduce data utility. This paper studies subject-level differential privacy for protecting clinical EEG-derived feature representations using Gaussian and Laplace perturbations. The proposed framework considers three deployment scenarios: client-side anonymization, centralized server-side anonymization, and decentralized local training. Following EEG preprocessing and feature extraction, Gaussian and Laplace perturbations are applied to the resulting patient-level EEG feature representations. The Laplace experiments evaluate the implemented noise scales, while the scales required for formal full-vector calibration are derived separately. The effects of both perturbations are assessed using statistical utility measures and a downstream machine-learning-based utility check. The results show that differentially private perturbation can be integrated into EEG processing workflows, but the selected mechanism, privacy parameters, and sensitivity calibration strongly influence data utility. The study highlights the practical privacy-utility trade-off in DP-based EEG feature anonymization and the challenges of preserving downstream utility in small and imbalanced clinical EEG datasets.
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