E-MagDiP：基于电磁的差分隐私用于基于脑电图的社区感知
E-MagDiP: Electro-Magnetic based Differential Privacy for EEG based Community Sensing
AI总结：
研究基于脑电图的社区感知中的隐私问题，提出E-MagDiP框架，利用外部无线电发射射频信号到脑电图耳机，在采集时扰动信号引入差分隐私噪声，无需用户修改，实现实用的脑电图社区感知差分隐私。
AI中文摘要：
基于脑电图的社区感知程序在全球范围内兴起，它利用聚合脑数据洞察学生和员工的注意力，但脑电图信号包含敏感个人信息，引发隐私担忧。差分隐私可保护个人同时保留聚合统计信息，但应用于脑电图数据具有挑战性。我们提出E-MagDiP框架，利用外部无线电将射频信号传输到脑电图耳机上，在采集时扰动信号以引入差分隐私噪声。据我们所知，E-MagDiP是首个将射频信号用于隐私而非攻击的框架，无需用户层面修改就能实现脑电图社区感知的实用差分隐私。
英文摘要：
EEG-based community sensing programs are emerging globally as a tool to leverage aggregated brain data to gain insights into attentiveness of students and employees. But these programs raise privacy concerns because EEG signals contain sensitive personal information. Differential Privacy (DP) can protect individuals while preserving aggregate statistics yet applying DP to EEG data is challenging as it requires user-level noise generation, which increases power and latency. Besides, most commercial EEG headsets cannot be modified to add such noise. We propose E-MagDiP, a framework that uses an external radio to transmit RF signals onto EEG headsets, perturbing signals at acquisition to induce DP noise. To the best of our knowledge, E-MagDiP is the first framework to use RF signals for privacy instead of attacks, enabling practical DP for EEG community sensing without any user-level modification.
