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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网络音频指纹的可行性和多样性研究

遗憾的是，之前对浏览器指纹的测量研究在其分析中很大程度上排除了基于 Web 音频 API 的指纹。我们通过对网络音频指纹识别机制的有效性进行首次系统研究来解决这个问题。我们专注于研究网络音频指纹的可行性和多样性特性。除了 3 个已知的音频指纹向量外，我们还设计并实现了 4 个新的音频指纹向量，这些向量通过获取通过不同方法生成的波形的 FFT 来工作。我们的研究分析了 2093 名网络用户的音频指纹，并对网络音频指纹的性质提出了新的见解。首先，我们展示了音频指纹向量，与其他先前的向量不同，显示出明显的变化无常，一些用户的浏览器在重复尝试中会泄露不同的指纹。然而，我们表明可以设计一种基于图的分析机制来共同考虑用户的所有不同指纹，从而制定稳定的指纹机制。我们的分析还表明，及时做到这一点是可能的。接下来，我们研究音频指纹的多样性，并将其与现有技术进行比较。我们的结果表明，音频指纹的多样性远低于其他向量，在 2093 个用户中只有 95 个不同的指纹。同时，进一步的分析表明，网络音频指纹识别可能会给现有的指纹识别机制带来可观的附加价值（就熵而言）。我们还表明，我们的结果与 W3C 提供的有关音频指纹识别的当前安全和隐私建议相矛盾。全面的，