用Transformer生成匿名脑电图，既防泄露又保分析效果
Bridging Privacy and Utility: Synthesizing anonymized EEG with constraining utility functions
- 基于Transformer的自编码器生成匿名脑电信号
- 匿名后重识别率大幅下降，睡眠分期任务性能基本不变
- 适合关注脑电隐私保护的研究者和开发者
脑电图（EEG）广泛用于记录脑活动，在睡眠阶段检测和神经疾病诊断等机器学习应用中表现良好。但已有研究证明，EEG数据存在身份重识别和隐私泄露风险。随着消费级脑电设备普及，用户隐私问题日益突出。为此，我们提出一种基于Transformer的自编码器，生成无法进行个体重识别但仍适用于特定机器学习任务的匿名脑电信号。通过自动睡眠分期任务评估匿名前后的数据，结果表明：在显著降低重识别可能性的同时，仍能保持对机器学习任务的有效性。
原文摘要 · Abstract (English)
Electroencephalography (EEG) is widely used for recording brain activity and has seen numerous applications in machine learning, such as detecting sleep stages and neurological disorders. Several studies have successfully shown the potential of EEG data for re-identification and leakage of other personal information. Therefore, the increasing availability of EEG consumer devices raises concerns about user privacy, motivating us to investigate how to safeguard this sensitive data while retaining its utility for EEG applications. To address this challenge, we propose a transformer-based autoencoder to create EEG data that does not allow for subject re-identification while still retaining its utility for specific machine learning tasks. We apply our approach to automatic sleep staging by evaluating the re-identification and utility potential of EEG data before and after anonymization. The results show that the re-identifiability of the EEG signal can be substantially reduced while preserving its utility for machine learning.
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