What Is Browser Fingerprinting? How It Works, Risks, and Countermeasures Explained

What Is Browser Fingerprinting?

Browser fingerprinting is a technique that identifies individuals by combining a wide range of information from their browser and device. Its distinctive feature is that, unlike cookies, it does not store information on the device but generates an identifier from environmental information at the time of access. Just as fingerprints differ from person to person, browser configurations also differ subtly from user to user. By collecting and matching these differences, it is possible to determine with high accuracy that the same person is revisiting a site. You can test this identification accuracy with Cover Your Tracks (https://coveryourtracks.eff.org/) published by the Electronic Frontier Foundation.

This technology does not rely on explicit consent or stored data. For this reason, as cookie restrictions have been tightened, it has attracted attention as an alternative tracking method. At the same time, concerns have been raised that users may be tracked without their knowledge.

How It Works: How Identification Is Performed

A fingerprint is generated by concatenating information obtained from multiple APIs and HTTP headers and hashing it to create an identifier. While a single piece of information has weak identifying power, combining dozens of attributes increases uniqueness. On the server side, information is collected on the first access and registered in a database. If the same combination appears on subsequent accesses, the visit is judged to be from the same device.

The identification process is completed through information collection via JavaScript and matching on the server side. No action is required on the user side, and collection is completed simply through normal web browsing. This ease of use has been a factor in expanding its use for advertising and fraud prevention.

Types of Information Collected

Information used for fingerprinting can be broadly classified into three layers.

• Information Derived from HTTP Headers

• User-Agent (browser name, version, OS)

• Accept-Language (language settings)

• IP address, time zone

• Information Derived from JavaScript APIs

• Screen resolution, window size, color depth

• List of installed fonts

• Presence of plugins and extensions

• WebGL renderer information, GPU type

• Advanced Techniques Using Rendering Characteristics

• Canvas fingerprinting: Renders an invisible image via the Canvas API and captures rendering differences for each device

• AudioContext fingerprinting: Uses differences in audio signal processing results

• Local IP address retrieval via WebRTC

Mozilla’s MDN Web Docs (https://developer.mozilla.org/ja/docs/Glossary/Fingerprinting) also explains how Canvas and WebGL are used for identification. While each piece of information alone is highly anonymous, combining them makes it possible to identify individuals with a granularity of one in several million.

Differences from Cookies

While cookies and fingerprinting have similar purposes, their natures are very different.

• Storage location: Cookies store text data on the device. Fingerprinting does not involve storage.
• Ease of deletion: Cookies can be deleted or blocked via browser settings. Fingerprinting has no concept of deletion because the device configuration itself serves as the identifier.
• Expiration: Cookies have an expiration date. Fingerprinting remains valid semi-permanently as long as the environment does not change.
• Regulatory scope: Cookies require consent under privacy laws in various countries. Fingerprinting remains an area where the interpretation of legal regulations has not yet been fully established.

Because of these differences, cookie countermeasures alone cannot completely block tracking. To protect privacy, countermeasures for both are necessary.

What Are the Risks: Privacy and Security Risks

The greatest risk is that tracking continues without the user’s intention. Even if cookies are deleted and private browsing mode is used, browsing history can be linked if the fingerprint remains the same. Browsing history across multiple sites may be used to infer interests, political orientation, and health status.

There is also malicious use from a security perspective. Cases have been reported where phishing sites mimic the fingerprint collection code of legitimate sites to obtain additional information. In addition, if an identifier is leaked, it can serve as a foothold for impersonation and targeted attacks.

On the other hand, whether a fingerprint itself qualifies as personal information is interpreted differently depending on the country and jurisdiction. Under the European General Data Protection Regulation (GDPR), it may be treated as protected information that can identify an individual. In Japan, the Personal Information Protection Commission’s guidelines also state that information that can identify an individual when collated with other information is considered personal information.

Use Cases for Fingerprinting:

Fraud Prevention and Advertising

Fingerprinting is widely used not only for malicious purposes but also for legitimate ones.

• Fraudulent login detection: Detects logins from an unusual device on e-commerce sites and financial services. If the fingerprint differs significantly from the registered one, additional authentication is required.
• Bot countermeasures: Detects large volumes of access from the same fingerprint and blocks scraping and fraudulent account creation.
• Alternative identifier for ad delivery: Amid the phasing out of third-party cookies, alternative identification methods are being explored, as shown by Google’s Privacy Sandbox (https://privacysandbox.google.com/). Fingerprinting is discussed as one of these options, but browsers are increasingly restricting it due to privacy concerns.
• Prevention of unauthorized content use: Detects sharing of multiple accounts with the same identifier on paid articles and video streaming services.

In practice, fingerprinting is often used not as a sole determinant but as a risk score combined with IP addresses and behavioral patterns. Because relying on a single judgment increases false positives, composite evaluation is the practical standard.

How to Check Your Own Fingerprint

You can check how easily you can be identified using publicly available verification sites.

• Cover Your Tracks (EFF): https://coveryourtracks.eff.org/ Determines how unique your browser is and how effective tracking protection is.
• AmIUnique: https://amiunique.org/ Displays a list of collected attributes and how rare they are within the overall population.
• BrowserLeaks: https://browserleaks.com/ Allows you to check detailed results for Canvas, WebGL, fonts, and more.

These sites display the User-Agent, Canvas hash, WebGL renderer, and other information. The display “one in x browsers” means that the larger the value of x, the higher the uniqueness. Comparing results between a work device and a personal device makes it easier to understand the effectiveness of countermeasures.

Effective Countermeasures and Their Limitations

Countermeasures against fingerprinting aim not to completely disable it but to reduce uniqueness.

• Use of privacy-focused browsers Brave (https://brave.com/privacy-updates/) and Firefox have built-in fingerprinting protection. They randomize access to Canvas and fonts to reduce identification accuracy.
• Use of Tor Browser It is designed so that all users have the same fingerprint. It offers the highest anonymity, but has limitations in rendering speed and compatibility with some sites.
• Minimizing extensions The combination of extensions itself becomes an identifying factor. It is effective to remove unnecessary extensions and keep the list in a common state.
• Combining VPN with fingerprinting countermeasures A VPN hides the IP address but cannot hide browser-derived information. Sufficient effect cannot be obtained without using both in combination.
• Restricting JavaScript Restricting scripts with tools like NoScript can block collection itself. However, many sites will no longer function properly.

It is also necessary to recognize the limitations of countermeasures. Canvas randomization may be detected as suspicious behavior because it returns a different value each time. In fraud detection by financial institutions, there are cases where access with frequently changing fingerprints is judged as high risk. Excessive spoofing on business systems may result in not being recognized as a legitimate user.

Comparison of Countermeasure Features by Browser

The status of major browsers is as follows.

• Brave: Fingerprinting protection is enabled by default. It randomizes or blocks access to Canvas, WebGL, and AudioContext. The protection level can be adjusted in settings.
• Firefox: Blocks known fingerprinting scripts with Enhanced Tracking Protection. Enabling privacy.resistFingerprinting in about:config results in behavior close to Tor Browser.
• Safari: Restricts tracking with Intelligent Tracking Prevention. It partially restricts access to APIs used for fingerprinting.
• Google Chrome: As part of Privacy Sandbox, it has indicated a policy to restrict the abuse of fingerprinting alongside the phasing out of third-party cookies. However, its standard randomization features are limited.

In the editorial team’s own testing, Brave and Tor Browser were superior in reducing uniqueness. On the other hand, in environments where the use of Chrome is mandatory for work, controlling it through organizational policy rather than relying on extensions provides greater operational stability.

Editorial Opinion

We believe that when evaluating countermeasures against browser fingerprinting, the balance between a high level of anonymity and usability should be emphasized. Techniques that aim for complete secrecy can cause broken page layouts and more complicated authentication. For business use, we assess that it is reasonable to make selections based on a balance between acceptable identification risk and operational burden.

What tends to be overlooked in practice is that fingerprinting countermeasures are not a cure-all. Installing too many extensions can conversely increase identifiability. We also see cases where disabling Canvas or spoofing the User-Agent is judged as anomalous by fraud detection systems. It seems fair to say that one should not overestimate the presence or absence of countermeasures and should assume a multi-layered defense.

We expect that in the next one to three years, conventional fingerprinting collection will be constrained by the spread of Privacy Sandbox and privacy-focused browsers. As alternatives, privacy-conscious identification methods such as the Topics API and Private State Tokens may become mainstream. We assess that as regulations are strengthened and browser-side countermeasures advance, the battle between tracking and privacy will become even more sophisticated.

References

• Electronic Frontier Foundation - Cover Your Tracks https://coveryourtracks.eff.org/
• MDN Web Docs - Fingerprinting https://developer.mozilla.org/ja/docs/Glossary/Fingerprinting
• Brave Software - Fingerprinting Protection https://brave.com/privacy-updates/
• Google Privacy Sandbox https://privacysandbox.google.com/
• AmIUnique - Learn how identifiable you are on the Internet https://amiunique.org/
• BrowserLeaks - Web Browser Fingerprinting https://browserleaks.com/