Fingerprint Identification combines dozens of browser attributes to create a unique and stable
visitorId. Attributes include screen resolution, operating system, IP address, loaded fonts, and other information that your browser can access. Individual attributes may not be accurate on their own, but when combined together the result is unique for most website visitors. For example, lots of browsers run on Macs, but not as many have an IP address in Nebraska, and fewer still have a Wingdings font – each of these attributes add up to help make a fingerprint. With the help of machine learning and a few other identifiers, Fingerprint Identification can recognize a returning browser through its
visitorId 99.5% of the time.
Identification accuracy for mobile devices
For mobile devices, the accuracy is even better because we identify mobile devices using signals that have higher longevity than those used for identifying browsers.
As part of its normal operation, Fingerprint Identification saves a cookie to each visitor’s browser.
We can identify cookied browsers with 100% accuracy and can use that as a reference to check the accuracy of all other identification methods. Below are the formulas for the accuracy of visitorIDs without a cookie, and the total accuracy of the Fingerprint Identification system.
Across all our customers, the
visitorId method gives an average accuracy of 99.5%.
Cookies are accurate but are not always available. Users can delete cookies or can browse in incognito mode. Fingerprint Identification is a better way to identify browsers because it can generate a unique
visitorId even without cookies. Another benefit is that Fingerprint Identification can store the history of every attribute that was used to identify the browsers. If a user switches to incognito mode, all those attributes remain the same and identification is easy. If a user upgrades their browser some attributes change, but enough remain the same that we can still identify the browser.
Sometimes a visitor to a website will have all the same browser attributes as another different visitor. If we aren’t able to find a difference between the two visitors, we may give that visitor’s browser the same
visitorId. This situation is known as a false positive. Fingerprint customers typically see a false-positive rate of around 0.5%.
FingerprintJS is a source-available, client-side browser fingerprinting library that combines browser attributes to generate a unique and stable hash.
The accuracy of FingerprintJS is less because, unlike Fingerprint Identification it does not store any history or use the server side identification methods.
FingerprintJS cannot tell apart two browsers of the same version from the same vendor on the same platform, as their attributes might be identical. But Fingerprint Identification distinguishes them thanks to additional server-side signals and its advanced matching algorithm.
For a complete list of differences, please visit Fingerprint Identification vs FingerprintJS.
In a recent browser fingerprinting study from KTH Royal Institute of Technology, only 33.6% of users were correctly identified. Other studies from the Electronic Frontier Foundation and Inria saw fingerprint accuracy between 80-90%, but those studies predate current web privacy policies and technologies. Fingerprint Identification's 99.5% accuracy is higher than any other service on the market. Through browser fingerprinting and other techniques, Fingerprint Identification provides best-in-class identification accuracy while complying with GDPR and CCPA rules.
Updated about 2 months ago