Sounds of Silence: A Study of Stability and Diversity of Web Audio Fingerprints

Abstract

Browser fingerprinting presents a grave threat to privacy as it allows user tracking even in private browsing modes. Prior measurement studies on HTML5-based fingerprinting have been limited to Canvas and WebGL but not Web Audio APIs. We aim to fill this gap by conducting the first large-scale systematic study of web audio fingerprints and studying their stability as well as diversity properties. Using MTurk and social media platforms, we collected 8 different audio fingerprints from 694 users. Firstly, we show that the audio fingerprints are unstable unlike other fingerprinting methods with some users having as many as 20 different fingerprints. Despite this, we show that audio fingerprinting can still be used as an effective fingerprinting vector as most fingerprints tend to repeat quite often. We devised a graph-based fingerprint matching mechanism to measure the diversity of audio fingerprints. Our results show that audio fingerprints are much less diverse with only 45 distinct fingerprints among 694 users