Eavesdropping on private conversations is one of the most common yet detrimental threats to privacy. A number of recent works have explored side-channels on smart devices for recording sounds without permission. This paper presents LidarPhone, a novel acoustic side-channel attack through the lidar sensors equipped in popular commodity robot vacuum cleaners. The core idea is to repurpose the lidar to a laser-based microphone that can sense sounds from subtle vibrations induced on nearby objects. LidarPhone carefully processes and extracts traces of sound signals from inherently noisy laser reflections to capture privacy sensitive information (such as speech emitted by a victim's computer speaker as the victim is engaged in a teleconferencing meeting; or known music clips from television shows emitted by a victim's TV set, potentially leaking the victim's political orientation or viewing preferences). We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and evaluate the feasibility of the attack through comprehensive real-world experiments. We use the prototype to collect both spoken digits and music played by a computer speaker and a TV soundbar, of more than 30k utterances totaling over 19 hours of recorded audio. LidarPhone achieves approximately 91% and 90% average accuracies of digit and music classifications, respectively.
|Title of host publication||SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||14|
|Publication status||Published - 2020 Nov 16|
|Event||18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, Japan|
Duration: 2020 Nov 16 → 2020 Nov 19
|Name||SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems|
|Conference||18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020|
|Period||20/11/16 → 20/11/19|
Bibliographical noteFunding Information:
This research was partially supported by a grant from Singapore Ministry of Education Academic Research Fund Tier 1 (R-252-000-A26-133).
© 2020 ACM.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Networks and Communications