NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems

2026-08-31

AI Summary

研究针对可穿戴EEG系统可能泄露敏感信息的问题，通过隐私感知的表示学习方法，在保持任务性能的同时降低了身份和属性推断的风险。

Abstract

Wearable EEG systems may expose sensitive information beyond their intended health function, creating substantial risks to neuroprivacy. In this work, we show that commonly used EEG features can reveal participant identity and demographic attributes in addition to supporting the intended cognitive task. Wearable EEG is increasingly being explored for cognitive monitoring, neurological assessment, and longitudinal digital-health applications, yet many systems assume that transmitting compact spectral or spatial features instead of raw EEG provides sufficient privacy protection. Using EEGMAT as a motivating case study, we find that compact EEG features achieve a balanced accuracy of 0.788 for cognitive-state classification while enabling gender, age, and subject-identity inference with balanced accuracies of 0.858, 0.789, and 0.692, respectively. We further show that privacy-aware representation learning preserves task performance at 0.781 while reducing these inference accuracies to 0.563, 0.467, and 0.206. These findings motivate purpose-limited representations and explicit privacy auditing in wearable neurohealth systems.

Problem

• neuroprivacy
• wearable EEG
• sensitive information
• demographic attributes
• privacy protection

Innovation

• Adversarial Representation Learning
• Privacy Protection
• Wearable EEG Systems
• Cognitive Task Performance