GULYÁS, Gábor György, Ph.D.

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Device fingerprinting by font-rendering differences

2015-07-21 | Gabor

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In 2012, we demonstrated that the OS can be fingerprinted by checking the presence of a greater variety of front (hey, we also have a paper on that). In addition, we showed this by using JavaScript only that was running from a website. This project seems to have more detailed results on this issue, as the authors went further than checking the presence of of a font: they checked how characters are rendered with a given font in different browser. This surely gives more details than 0/1, and according to their results they could use this information solely to make 34% of their submissions uniquely identifiable:

We describe a web browser fingerprinting technique based on measuring the onscreen dimensions of font glyphs. Font rendering in web browsers is affected by many factors—browser version, what fonts are installed, and hinting and antialiasing settings, to name a few— that are sources of fingerprintable variation in end-user systems. We show that even the relatively crude tool of measuring glyph bounding boxes can yield a strong fingerprint, and is a threat to users' privacy. Through a user experiment involving over 1,000 web browsers and an exhaustive survey of the allocated space of Unicode, we find that font metrics are more diverse than User-Agent strings, uniquely identifying 34% of participants, and putting others into smaller anonymity sets. Fingerprinting is easy and takes only milliseconds. We show that of the over 125,000 code points examined, it suffices to test only 43 in order to account for all the variation seen in our experiment. Font metrics, being orthogonal to many other fingerprinting techniques, can augment and sharpen those other techniques.

We seek ways for privacy-oriented web browsers to reduce the effectiveness of font metric–based fingerprinting, without unduly harming usability. As part of the same user experiment of 1,000 web browsers, we find that whitelisting a set of standard font files has the potential to more than quadruple the size of anonymity sets on average, and reduce the fraction of users with a unique font fingerprint below 10%. We discuss other potential countermeasures.

You can find the paper here.

Tags: fingerprint, tracking, web bug, font, device identifier, device fingerprint, interesting paper

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