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Canvas fingerprinting

Browser fingerprinting technique

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Through JavaScript

Description

Canvas fingerprinting works by exploiting the HTML5 canvas element. As described by Acar et al.:

When a user visits a page, the fingerprinting script first draws text with the font and size of its choice and adds background colors (1). Next, the script calls Canvas API’s ToDataURL method to get the canvas pixel data in dataURL format (2), which is basically a Base64 encoded representation of the binary pixel data. Finally, the script takes the hash of the text-encoded pixel data (3), which serves as the fingerprint ...

Which graphics processing unit (GPU) or graphics driver is installed causes the fingerprint variation. The fingerprint can be stored and shared with advertising partners to identify users when they visit affiliated websites. A profile can be created from the user's browsing activity, allowing advertisers to target advertise to the user's inferred demographics and preferences.

By January 2022, the concept was extended to fingerprinting performance characteristics of the graphics hardware, called DrawnApart by the researchers.

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.

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