A comprehensive study of how fingerprintable users are

Re:A comprehensive study of how fingerprintable users are by _factor on Tuesday November 10, 2026 @03:39PM (#50445996)

I wonder if this took into account GPU render-time fingerprinting? The minute timing differences in how you render an image are still used to identify you.

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Re:A comprehensive study of how fingerprintable users are by magicalhippo on Tuesday November 10, 2026 @03:59PM (#50446251)

[delayed]

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Re:A comprehensive study of how fingerprintable users are by LoganDark on Tuesday November 10, 2026 @07:12PM (#50448148)

This reminds me of some fucking bullshit about how browsers silently store canvas pixel data as premultiplied and try to transparently convert everything you put in and everything you take out, but silently corrupt color values in the process.

https://github.com/nagadomi/nunif/issues/32#issuecomment-153...

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Re:A comprehensive study of how fingerprintable users are by raphlinus on Tuesday November 10, 2026 @04:59PM (#50446881)

If you can run compute shader code, the fingerprinting and side channel possibilities become very rich. [1] is one paper among many on this topic.

[1]: https://dl.acm.org/doi/10.1145/3634737.3656283

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Re:A comprehensive study of how fingerprintable users are by tomrittervg on Tuesday November 10, 2026 @09:49PM (#50449231)

It does not - there are a lot of these sorts of techniques, but we specifically modeled what commercial fingerprinters do in the wild to provide a realistic number. I have not seen a commercial fingerprinter do those sorts of timing-based fingerprinting techniques. (Doesn't mean there isn't one, just that I've looked at more scripts that your average person and haven't seen it.)

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Re:A comprehensive study of how fingerprintable users are by _factor on Tuesday November 10, 2026 @11:52PM (#50449864)

Oracle has a product that does just this commercially.

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