For those of you who came here expecting acoustic fingerprinting, realize this is a different class of algorithms related to locality sensitive hashing (LSH), ala Shazam, etc.
If you look at the page [here](https://audiofingerprint.openwpm.com/), you'll see they are merely looking at the audio processing capabilities for your browser's, which is tied to your hardware microphone's / browser version's specs. Things like number of channels, FFT size, etc.
In summary, this is only invasive so far as knowing your laptop model or browser version, not actual recorded voice (you have to call `getUserMedia()` for that IIRC).
The relevant section of the study is "6.4 AudioContext Fingerprinting", on page 15. Notably, they link to an online tool they developed that demonstrates this fingerprinting technique [1]
Article is just recompilation of articles from other news sites, there is no explanation how it works and there is link to "acoustic fingerprint" article on Wikipedia which is about entirely different thing.
For those of you who came here expecting acoustic fingerprinting, realize this is a different class of algorithms related to locality sensitive hashing (LSH), ala Shazam, etc.
If you look at the page [here](https://audiofingerprint.openwpm.com/), you'll see they are merely looking at the audio processing capabilities for your browser's, which is tied to your hardware microphone's / browser version's specs. Things like number of channels, FFT size, etc.
In summary, this is only invasive so far as knowing your laptop model or browser version, not actual recorded voice (you have to call `getUserMedia()` for that IIRC).
For more information on acoustic fingerprinting, see [here](https://en.wikipedia.org/wiki/Acoustic_fingerprint) and [here](http://willdrevo.com/fingerprinting-and-audio-recognition-wi...).