Fingerprinting (often referred to as browser fingerprinting) is a term associated with the process of collecting a broad range of computer and browser information through a user’s web browser to uniquely identify a user and/or device. Fingerprints are used to identify a device when other persistent identifiers such as cookies cannot be read or stored by a website. A fingerprint is created by combining various data points from your device and/or browser that are accessible to every website when you visit their page. This could include the browser version, installed browser extensions, and plugins (including their versions), hardware properties, list of fonts, canvas and WebGL, HW benchmarking, language, time zones, OS version, screen characteristics, and menu bars.
Statistically, there is a very small chance that two or more devices will have identical set-ups and configurations, you can view how unique your configurations and setup are here. Fingerprints are especially strong given they can detect a device over an extended period even if certain parameters of the fingerprint are altered, with IP addresses changed, or even behind a VPN.
While fingerprinting techniques were initially created as a method for banks to detect fraud and prevent identity theft, today, they are used to track users across websites in order to compile long-term records of individuals’ browsing histories, and deliver targeted advertising or targeted exploits to users; thus, raising serious privacy concerns. For this reason browsers (Safari, Chrome, and Firefox to name a few) have recently begun making changes intended to limit the amount of data they expose to websites, and to make users look more alike, creating a kind of “herd immunity”.
The good news is that mobile browsers provide much less data, and already provide the aforementioned “herd immunity” against the type of fingerprinting common on desktops. iOS devices especially are much less fragmented, making them greatly immune to fingerprinting.
AppsFlyer’s Probabilistic Modeling:
Probabilistic modeling is a statistical technique to estimate campaign performance and can’t be used to uniquely identify a user and/or device. It leverages machine learning to estimate campaign performance without compromising on privacy. Unlike fingerprinting, which seeks to maximize captured data points from each user to create a unique identifier that can be used to track users over an extended period and across websites, AppsFlyer’s probabilistic modeling seeks to do the exact opposite; minimize the captured data points and prevent the ability to create a unique persistent or permanent identifier that can be used to identify a user and/or device.
Where fingerprints are used to create detailed profiles of users to enable precise targeting (at any point in time and on any site), probabilistic modeling is employed with the sole purpose of estimating campaign performance of paid and owned media (such as websites, social media platforms, emails, and user referrals).
Probabilistic modeling measures the app developers’ owned creative and campaign details, not data from the apps in which the ads are served. Moreover, in most cases, the app in which the ad was served is unknown.
Probabilistic modeling relies on very few data points that change frequently. For this reason, machine learning and statistical estimation techniques are used (as opposed to creating and matching unique ID’s) and why lookback windows cannot be defined.
As described above probabilistic modeling is a privacy-centric method to estimate ad campaign performance. It is very different from fingerprinting. It does not generate a unique ID that is persistent or permanent or that can uniquely identify any device across sites or apps over an extended period. It is not used for targeting or profiling. In fact, probabilistic modeling is one of the most privacy-friendly ways for attribution and estimating campaign performance.
The following table presents an easy way to view the significant differences between traditional browser fingerprinting and probabilistic modeling:
| ||Fingerprinting||AppsFlyer’s Probabilistic Modeling|
|Equivalent to unique and/or persistent ID||Yes||No|
|Can be used to uniquely identify a device||Yes||No|
|Can be used to track users across sites/apps (cross-site tracking)||Yes||No|
|Relies on vast amounts of user-device and browser data||Yes||No|
|Can be used to create profiles||Yes||No|
|Can be used to target users||Yes||No|
|Can identify a device even when users hide behind VPNs or alternate IPs||Yes||No|
|Privacy||Can be leveraged in intrusive ways||Friendly|