![]() The key to identifying those most likely to commit fraud is either by past activity, or by associating specific patterns of use with a higher likelihood of fraudulence.īrowser fingerprinting technologies are a cornerstone technology for developer-led fraud prevention that cuts through spoofing attempts to accurately identify users, and it can do this without requiring additional permissions from the user. ![]() Our open source browser fingerprinting library has over 12K stars on Github and is used by 8,000+ websites. ![]() How Fingerprinting Works Identifying a Vehicle Fingerprinting techniques on their own have been found to be over 90% accurate in correctly identifying a unique user in the browser, and when used in conjunction with usage history, fuzzy matching, and probability engines, this accuracy can be further improved. Here’s an analogy: let’s say you’re a detective in a large city trying to find one specific car suspected of being a bank robber’s getaway vehicle, as captured by a security camera. To find this car your plan is to go to a busy intersection and take note of all the details of passing cars until you find one that matches the vehicle on the security camera. If the security camera caught some basic details (or signals) about the car, you’ll be able to narrow your search considerably: Ideally, you would like to be able to uniquely identify the car, such that only one vehicle in the city matches your description, otherwise you’ll have to question multiple drivers. With these signals, you may be able to uniquely identify the vehicle right away, especially if any of the specifics are particularly rare. However, in a city with millions of drivers, there may be hundreds of red Ford F-150 trucks with standard-issue tires. The more standard the combination of signals, the harder it is to get a unique match.
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