E-MagDiP: Electro-Magnetic based Differential Privacy for EEG based Community Sensing

2026-07-28

AI Summary

This work addresses the challenge of deploying conventional differential privacy mechanisms on commercial electroencephalography (EEG) devices, which are typically closed-source and cannot be modified to inject noise at the user end. The authors propose a novel user-level differential privacy paradigm that requires no alterations to the EEG hardware: instead, it leverages an external radio transmitter to emit radio-frequency (RF) signals that induce physical-layer perturbations during EEG acquisition. This approach uniquely repurposes RF interference—traditionally viewed as a security threat—as a mechanism for privacy preservation. By doing so, it achieves hardware-agnostic, non-invasive privacy protection that effectively safeguards individual-sensitive neural data while preserving the utility of aggregate statistical analyses.

Abstract

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.

Problem

• Differential Privacy
• EEG
• Community Sensing
• Privacy
• RF signals

Innovation

• Electro-Magnetic Perturbation
• Differential Privacy
• EEG Sensing
• RF-based Privacy
• Community Sensing