mmSpy: Spying Phone Calls using mmWave Radars

Suryoday Basak, Mahanth Gowda

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

7 Scopus citations


This paper presents a system mmSpy that shows the feasibility of eavesdropping phone calls remotely. Towards this end, mmSpy performs sensing of earpiece vibrations using an off-the-shelf radar device that operates in the mmWave spectrum (77GHz, and 60GHz). Given that mmWave radars are becoming popular in a number of autonomous driving, remote sensing, and other IoT applications, we believe this is a critical privacy concern. In contrast to prior works that show the feasibility of detecting loudspeaker vibrations with larger amplitudes, mmSpy exploits smaller wavelengths of mmWave radar signals to detect subtle vibrations in the earpiece devices used in phonecalls. Towards designing this attack, mmSpy solves a number of challenges related to non-availability of large scale radar datasets, systematic correction of various sources of noises, as well as domain adaptation problems in harvesting training data. Extensive measurement-based validation achieves an endto-end accuracy of 83-44% in classifying digits and keywords over a range of 1-6ft, thereby compromising the privacy in applications such as exchange of credit card information. In addition, mmSpy shows the feasibility of reconstruction of the audio signals from the radar data, using which more sensitive information can be potentially leaked.

Original languageEnglish (US)
Title of host publicationProceedings - 43rd IEEE Symposium on Security and Privacy, SP 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages18
ISBN (Electronic)9781665413169
StatePublished - 2022
Event43rd IEEE Symposium on Security and Privacy, SP 2022 - San Francisco, United States
Duration: May 23 2022May 26 2022

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011


Conference43rd IEEE Symposium on Security and Privacy, SP 2022
Country/TerritoryUnited States
CitySan Francisco

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Software
  • Computer Networks and Communications


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