Prior measurement studies on browser fingerprinting have unfortunately largely excluded Web Audio API-based fingerprinting in their analysis. We address this issue by conducting the first systematic study of effectiveness of web audio fingerprinting mechanisms. We focus on studying the feasibility and diversity properties of web audio fingerprinting. Along with 3 known audio fingerprinting vectors, we designed and implemented 4 new audio fingerprint vectors that work by obtaining FFTs of waveforms generated via different methods. Our study analyzed audio fingerprints from 2093 web users and presents new insights into the nature of Web Audio fingerprints. First, we show that audio fingeprinting vectors, unlike other prior vectors, reveal an apparent fickleness with some users browsers giving away differing fingerprints in repeated attempts. However, we show that it is possible to devise a graph-based analysis mechanism to collectively consider all the different fingerprints of users and thus craft a stable fingerprinting mechanism. Our analysis also shows that it is possible to do this in a timely fashion. Next, we investigate the diversity of audio fingerprints and compare this with prior techniques. Our results show that audio fingerprints are much less diverse than other vectors with only 95 distinct fingerprints among 2093 users. At the same time, further analysis shows that web audio fingerprinting can potentially bring considerable additive value (in terms of entropy) to existing fingerprinting mechanisms. We also show that our results contradict the current security and privacy recommendations provided by W3C regarding audio fingerprinting. Overall, our systematic study allows browser developers to gauge the degree of privacy invasion presented by audio fingerprinting thus helping them take a more informed stance when designing privacy protection features in the future.
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