Kinetic Song Comprehension: Deciphering Personal Listening Habits via Phone Vibrations
This reveals a privacy vulnerability where malicious entities could exploit motion sensor data to intrude on user preferences, potentially for financial or political gain.
The paper tackles the problem of inferring personal music listening habits by using phone vibrations to detect and classify songs, achieving over 80% accuracy for a corpus of 100 songs.
Music is an expression of our identity, showing a significant correlation with other personal traits, beliefs, and habits. If accessed by a malicious entity, an individual's music listening habits could be used to make critical inferences about the user. In this paper, we showcase an attack in which the vibrations propagated through a user's phone while playing music via its speakers can be used to detect and classify songs. Our attack shows that known songs can be detected with an accuracy of just under 80%, while a corpus of 100 songs can be classified with an accuracy greater than 80%. We investigate such questions under a wide variety of experimental scenarios involving three surfaces and five phone speaker volumes. Although users can mitigate some of the risk by using a phone cover to dampen the vibrations, we show that a sophisticated attacker could adapt the attack to still classify songs with a decent accuracy. This paper demonstrates a new way in which motion sensor data can be leveraged to intrude on user music preferences without their express permission. Whether this information is leveraged for financial gain or political purposes, our research makes a case for why more rigorous methods of protecting user data should be utilized by companies, and if necessary, individuals.