Spying with your robot vacuum cleaner: Eavesdropping via lidar sensors

Sriram Sami, Yimin Dai, Sean Rui Xiang Tan, Nirupam Roy, Jun Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages354-367
Number of pages14
ISBN (Electronic)9781450375900
DOIs
Publication statusPublished - 2020 Nov 16
Event18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, Japan
Duration: 2020 Nov 162020 Nov 19

Publication series

NameSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems

Conference

Conference18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
Country/TerritoryJapan
CityVirtual, Online
Period20/11/1620/11/19

Bibliographical note

Funding Information:
This research was partially supported by a grant from Singapore Ministry of Education Academic Research Fund Tier 1 (R-252-000-A26-133).

Publisher Copyright:
© 2020 ACM.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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