Abstract
Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data. The analysis highlights that browser fingerprinting poses a complex challenge from both technical and privacy perspectives, as users often have no control over the collection and use of their data. In addition, it raises significant privacy concerns as users are often tracked without their knowledge or consent.
Methods of Browser Fingerprinting
- A. HTTP Header Attributes
- B. Enumeration of Browser Plugins
- C. Canvas Fingerprinting
- D. WebGL Fingerprinting
- E. Audio Fingerprinting
- F. Font Fingerprinting
- G. Screen Fingerprinting
- H. WebRTC Fingerprinting
- I. CSS Fingerprinting
- J. Additional JavaScript Attributes
- K. Advanced Techniques Using Machine Learning
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