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

arXiv: https://arxiv.org/abs/2609.11777 · HTML: https://mlsanity.com/paper/2609.11777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Do Gradient Inversion Attacks Make Federated Learning Unsafe?](https://mlsanity.com/paper/2202.06924/) (2022)
- [Cross-validation failure: small sample sizes lead to large error bars](https://mlsanity.com/paper/1706.07581/) (2017)
- [Domain-Invariant Representation Learning from EEG with Private Encoders](https://mlsanity.com/paper/2201.11613/) (2022)
- [Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications](https://mlsanity.com/paper/2506.00660/) (2025)
- [User-wise Perturbations for User Identity Protection in EEG-Based BCIs](https://mlsanity.com/paper/2411.10469/) (2024)
- [User Identity Protection in EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09854/) (2024)
- [Privacy-Preserving Brain-Computer Interfaces: A Systematic Review](https://mlsanity.com/paper/2412.11394/) (2024)
- [Global Differential Privacy for Distributed Metaverse Healthcare Systems](https://mlsanity.com/paper/2308.04439/) (2023)
- [Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data](https://mlsanity.com/paper/2607.28191/) (2026)

## Abstract (arXiv)

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.