Canvas fingerprinting works by exploiting the
HTML5 canvas element. As described by Acar et al. in:
Uniqueness Since the fingerprint is primarily based on the browser, operating system, and installed graphics hardware, it does not, on its own, uniquely identify users. In a small-scale study with 294 participants from
Amazon's Mechanical Turk, an experimental
entropy of 5.7 bits was observed. The authors of the study suggest more entropy could likely be observed in the wild and with more patterns used in the fingerprint. While not sufficient to identify individual users by itself, this fingerprint could be combined with other entropy sources to provide a unique identifier. It is claimed that because the technique is effectively fingerprinting the GPU, the entropy is "orthogonal" to the entropy of previous browser fingerprint techniques such as
screen resolution and browser
JavaScript capabilities. Much more unique identification becomes possible with DrawnApart, published in 2022, which was shown to boost tracking duration of individual fingerprints by 67% when used to enhance other methods. ==History==