Device-Independent Smartphone Eavesdropping Jointly Using Accelerometer and Gyroscope

Eavesdropping via inertial measurement units (IMUs) has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs, including accelerometers and gyroscopes, which require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85$\sim$255 Hz) and inertial measurements (0$\sim$100 Hz). Nevertheless, we observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, namely InertiEAR, to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages accelerometers and gyroscopes jointly to eavesdrop on both top and bottom speakers in smartphones. We exploit coherence between responses of the built-in accelerometer and gyroscope using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones. InertiEAR achieves the recognition accuracy of 78.8% with the cross-device accuracy of up to 60.9% among 12 smartphones.

1 Introduction

Privacy has always been a pivotal issue. People express increasing concerns over privacy protection, especially over eavesdropping via smartphones. Various sensors in smartphones intelligently gather information from the real world. However, those sensors risk malicious abuse. To resist privacy leakage, individuals consciously perform rigorous access control over explicitly privacy-related sensors such as microphones, cameras, and GPS.