tjazsilovsek/fingerprinting

Project showcasing some fingerprinting techniques. It is a simple POC.

What are Fingerprints?

Fingerprints in this project refer to unique digital identifiers generated for each user visiting the website. Just like physical fingerprints uniquely identify individuals, these digital fingerprints are specific to each user's browser and device.

How do Fingerprints Work?

The fingerprints are generated using a combination of methods and factors:

1. Canvas Fingerprinting:

   The Canvas Fingerprint is generated by drawing shapes on the page using the Canvas API. The specific way each user's browser renders these shapes creates a distinct fingerprint unique to that browser.

2. Audio Fingerprinting:

   The Audio Fingerprint is generated by utilizing the Web Audio API. The code captures information about the user's audio hardware, contributing to a second unique fingerprint.

3. Additional Factors:

   Beyond the methods demonstrated, additional factors are considered for fingerprint generation. These factors include user agent, language preferences, platform (e.g., Windows, macOS, Linux), screen size and resolution, color depth, and timezone offset. These factors enhance the fingerprint's uniqueness.

The Significance of Fingerprints

Fingerprints play a crucial role in recognizing returning users within the same browser. When a user visits the website again using the same browser, the fingerprints will match, enabling the site to identify them as a returning visitor.

Cross-Browser Variation

It's important to understand that fingerprints may differ between different browsers or devices. Thus, if a user accesses the website from a different browser or device, they will have different fingerprints.

Combining Multiple Fingerprinting Methods and Additional Factors

The uniqueness of fingerprints stems from the ability to combine multiple fingerprinting methods and incorporate various factors related to the user's device and browsing behavior. Multiple fingerprinting methods, including Canvas and Audio Fingerprinting, along with additional factors like screen size, installed fonts, language preferences, and user agent information, create highly unique and specific fingerprints for each user. This ensures a higher level of accuracy in recognizing returning users within the same browser.

Privacy Considerations

While fingerprinting can be valuable for recognizing returning users and personalizing experiences, it's essential to be mindful of privacy implications. Collecting a wide range of information for fingerprinting can raise privacy concerns and lead to potential user tracking. As a responsible developer, ensure user consent and transparency regarding data collection.

License

This project is licensed under the MIT License.

How to use this project

To use this project, you will need to:

1. Install the dependencies by running yarn install.
2. Build the project by running yarn build.
3. Open index.html in your browser to see the fingerprinting in action.