临床脑电图数据隐私保护研究：差分隐私框架下的特征匿名化
Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
朋友，这是篇关于如何用差分隐私技术保护临床脑电图数据的论文，作者测试了高斯和拉普拉斯两种噪声方法，看看哪种能更好平衡隐私和实用性。
这篇论文提出了一种使用高斯和拉普拉斯噪声扰动的方法来保护临床脑电图（EEG）数据中的患者特征信息。研究评估了这种差分隐私（DP）方法在三种部署场景下的效果，包括客户端匿名化、中心化服务器处理和去中心化本地训练。实验表明，虽然差分隐私可以集成到EEG处理流程中，但噪声机制、隐私参数和敏感性校准的选择会显著影响数据效用。
Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
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.
