保护脑机接口中身份、性别和经验的多重隐私
Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces
- 对原始脑电数据添加扰动，实现隐私保护
- 多重隐私信息分类准确率大幅下降，任务性能几乎不变
- 适合关注脑机接口安全的科研与医疗从业者
脑机接口（BCI）通过直接连接大脑与外部设备实现通信。在非侵入式BCI中，脑电图（EEG）因其便捷性和低成本成为首选信号。已有应用包括神经康复、文本输入和游戏等。然而，EEG信号蕴含丰富个人特征，存在严重隐私风险。本文表明，用户身份、性别及BCI使用经验均可从EEG数据中轻易推断，构成重大隐私威胁。为此，我们设计了数据扰动方法，将原始EEG转换为隐私保护后的数据，在隐藏用户身份、性别和使用经验的同时，几乎不损害主任务的分类性能。实验结果显示，隐私保护后数据显著降低三类私密信息的分类准确率，而主任务准确率基本不受影响，实现了多类型隐私的同步保护。
原文摘要 · Abstract (English)
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs.
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