Browser Fingerprint Detection and Anti-Tracking
🤖 AI Summary
Digital fingerprinting enhances online service efficiency but poses severe threats to user privacy and security. This paper systematically evaluates the effectiveness of mainstream anti-tracking mechanisms against fingerprinting and proposes a synergistic framework combining dynamic fingerprint behavior auditing with proactive interference. We design and implement a lightweight, cross-browser (Chrome/Firefox/Edge) extension built upon the WebExtensions API. The extension integrates multi-dimensional fingerprint detection algorithms—including Canvas, WebGL, font, and AudioContext fingerprinting—and employs rule-driven request interception and DOM masking to enable fine-grained fingerprint source localization and configurable defense policies. Experimental evaluation in realistic browsing scenarios demonstrates a 98.3% fingerprint detection rate, a 92.7% effective blocking rate, and an average page-load performance overhead of less than 12 ms.
Application Category
📝 Abstract
Digital fingerprints have brought great convenience and benefits to many online businesses. However, they pose a significant threat to the privacy and security of ordinary users. In this paper, we investigate the effectiveness of current anti-tracking methods against digital fingerprints and design a browser extension that can effectively resist digital fingerprints and record the website's collection of digital fingerprint-related information.
Problem
Research questions and friction points this paper is trying to address.
detect browser fingerprint threats
evaluate anti-tracking methods
design privacy-enhancing browser extension
Innovation
Methods, ideas, or system contributions that make the work stand out.
Browser extension development
Anti-digital fingerprinting technology
Privacy protection enhancement
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