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

• URL: http://arxiv.org/abs/2609.11777v1
• Date: Thu, 10 Sep 2026 16:31:11 GMT
• Title: Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
• Abstract summary: This paper studies subject-level differential privacy for protecting clinical EEG-derived feature representations using Gaussian and Laplace perturbations.<n>The proposed framework considers three deployment scenarios: client-side anonymization, centralized server-side anonymization, and decentralized local training.<n>The results show that differentially private perturbation can be integrated into EEG processing, but the selected mechanism, privacy parameters, and sensitivity calibration strongly influence data utility.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection [0.0]
  LookAroundNet is a transformer-based seizure detector that uses a wider temporal window of EEG data to model seizure activity.<n>We evaluate the proposed method on multiple EEG datasets spanning diverse clinical environments.
  arXiv  Detail & Related papers  (2026-01-09T18:52:24Z)
• Bridging Privacy and Utility: Synthesizing anonymized EEG with constraining utility functions [0.5863360388454261]
  We propose a transformer-based autoencoder to create EEG data that does not allow for subject re-identification.<n>We show that the re-identifiability of the EEG signal can be substantially reduced while preserving its utility for machine learning.
  arXiv  Detail & Related papers  (2025-09-24T18:02:41Z)
• Clinically-guided Data Synthesis for Laryngeal Lesion Detection [2.573786844054239]
  This study introduces a novel approach that exploits a Latent Diffusion Model (LDM) coupled with a ControlNet adapter to generate laryngeal endoscopic image-annotation pairs.<n>The proposed approach can be leveraged to expand training datasets for CADx/e models, empowering the assessment process in laryngology.
  arXiv  Detail & Related papers  (2025-08-08T09:55:54Z)
• Differentially Private Distributed Inference [2.4401219403555814]
  Healthcare centers collaborating on clinical trials must balance knowledge sharing with safeguarding sensitive patient data.<n>We address this challenge by using differential privacy (DP) to control information leakage.<n>Agents update belief statistics via log-linear rules, and DP noise provides plausible deniability and rigorous performance guarantees.
  arXiv  Detail & Related papers  (2024-02-13T01:38:01Z)
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging [47.99192239793597]
  We evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.
  arXiv  Detail & Related papers  (2023-02-03T09:49:13Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Assessing Robustness of EEG Representations under Data-shifts via Latent Space and Uncertainty Analysis [0.29998889086656577]
  We develop diagnostic measures to detect potential pitfalls during deployment without assuming access to external data. Specifically, we focus on modeling realistic data shifts in electrophysiological signals (EEGs) via data transforms. We conduct experiments on multiple EEG feature encoders and two clinically relevant downstream tasks using publicly available large-scale clinical EEGs.
  arXiv  Detail & Related papers  (2022-09-22T19:26:09Z)
• Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation [54.88777449903538]
  We introduce a novel textithybrid automatic differentiation (AD) system for sensitivity analysis. This enables modelling the sensitivity of arbitrary differentiable function compositions, such as the training of neural networks on private data. Our approach can enable the principled reasoning about privacy loss in the setting of data processing.
  arXiv  Detail & Related papers  (2021-07-09T07:19:23Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• RDP-GAN: A R\'enyi-Differential Privacy based Generative Adversarial Network [75.81653258081435]
  Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. However, when GANs are applied on sensitive or private training examples, such as medical or financial records, it is still probable to divulge individuals' sensitive and private information. We propose a R'enyi-differentially private-GAN (RDP-GAN), which achieves differential privacy (DP) in a GAN by carefully adding random noises on the value of the loss function during training.
  arXiv  Detail & Related papers  (2020-07-04T09:51:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.