Pitchln: eavesdropping via intelligible speech reconstruction using non-acoustic sensor fusion

Despite the advent of numerous Internet-of-Things (IoT) applications, recent research demonstrates potential side-channel vulnerabilities exploiting sensors which are used for event and environment monitoring. In this paper, we propose a new side-channel attack, where a network of distributed non-acoustic sensors can be exploited by an attacker to launch an eavesdropping attack by reconstructing intelligible speech signals. Specifically, we present PitchIn to demonstrate the feasibility of speech reconstruction from non-acoustic sensor data collected offline across networked devices. Unlike speech reconstruction which requires a high sampling frequency (e.g., > 5 KHz), typical applications using non-acoustic sensors do not rely on richly sampled data, presenting a challenge to the speech reconstruction attack. Hence, PitchIn leverages a distributed form of Time Interleaved Analog-Digital-Conversion (TIADC) to approximate a high sampling frequency, while maintaining low per-node sampling frequency. We demonstrate how distributed TI-ADC can be used to achieve intelligibility by processing an interleaved signal composed of different sensors across networked devices. We implement PitchIn and evaluate reconstructed speech signal intelligibility via user studies. PitchIn has word recognition accuracy as high as 79%. Though some additional work is required to improve accuracy, our results suggest that eavesdropping using a fusion of non-acoustic sensors is a real and practical threat.