Digital Footprint Detection: A Client-Side Browser Security Framework for Real-Time Tracker Auditing, Device Fingerprint Mitigation, and Dynamic Privacy Risk Assessment

Abstract

Modern web applications subject end-users to pervasive tracking mechanisms, including third-party stateful cookies, persistent local storage beacons, cache-based ETag tracking, cross-site script injections, and stateless hardware fingerprinting. While conventional privacy extensions rely predominantly on static blocklists, they often fail to quantify real-time data exposure, lack transparent risk metrics, and offer minimal visibility into sophisticated device fingerprinting and DNS leakage. This paper presents the design, implementation, and empirical evaluation of Digital Footprint Detection, an advanced client-side browser security extension engineered on Google Chrome’s Manifest V3 service-worker architecture. The framework introduces a multi-tier detection engine combining DOM-level Canvas and WebGL API interception, dual-layer DNS leakage detection via DoH resolvers (Cloudflare and Google/Akamai), behavioral biometric telemetry, and automated token cryptanalysis (including JWT decoding). Furthermore, the extension formulates a dynamic multi-factor risk assessment model that computes a composite 10-point privacy score across seven weighted attributes per cookie and tracking vector. To actively safeguard user privacy, an automated countermeasure subsystem enforces real-time header stripping, ad-domain network blocking, automated high-risk cookie remediation, and synthetic canvas noise injection. Experimental validation across high-traffic commercial and media domains demonstrates that the proposed framework reliably intercepts 100% of tested canvas fingerprint attempts, successfully neutralizes cross-site tracking vectors, and delivers low-overhead, real-time privacy auditing without degrading page render performance.

Introduction

Digital Footprint Detection is a privacy-focused browser security framework designed to identify, analyze, and prevent online tracking. Modern websites and third-party trackers collect users’ browsing histories, device information, and online behavior through cookies, browser storage, cache identifiers, and device fingerprinting techniques. Existing privacy tools often rely on static ad-blocking rules and provide limited transparency about the risks users face.

The literature survey reviews research on browser fingerprinting, canvas tracking, cross-site tracking, and privacy protection. It identifies four major limitations in existing solutions: insufficient integration of different tracking detection methods, limited analysis of cookie contents, lack of dynamic privacy risk scoring, and challenges in adapting to Chrome’s Manifest V3 architecture.

To address these issues, the proposed system introduces a client-side browser extension that operates without sending telemetry to an external server. It combines network request monitoring, cookie analysis, device fingerprint detection, DNS leak auditing, risk assessment, and automated protection in one platform.

The system consists of six main components:

• Manifest V3 Service Worker: Manages browser events and network monitoring efficiently.

• Fingerprint Detection: Identifies potential tracking attempts using Canvas, WebGL, AudioContext, and device characteristics.

• Traffic Filtering: Detects and blocks selected tracking requests and unauthorized tracking cookies.

• Privacy Risk Assessment: Calculates a score from 0 to 10 based on seven factors, including cookie security settings, expiration time, tracker affiliation, and payload size.

• Automated Remediation: Removes cookies from domains whose risk score reaches the configured threshold of 8 and provides anti-fingerprinting protection.

• Analytics Dashboard: Displays risk trends, tracking exposure, remediation statistics, and fingerprint information, with options to export reports.

The methodology includes dual-layer DNS auditing using DNS-over-HTTPS services, analysis of browser and behavioral fingerprints, and client-side decoding of URL-encoded strings, Base64 data, and JSON Web Tokens (JWTs) to help reveal potentially sensitive tracking information.

Conclusion

Digital Footprint Detection delivers an integrated, lightweight browser security platform tailored for modern web privacy defense. By uniting Manifest V3 service-worker request filtering, DOM-level canvas monkey-patching, dual-layer DoH DNS verification, token cryptanalysis, and dynamic multi-factor risk assessment into a single client-side tool, the system bridges the gap between passive blocklists and active defense. Empirical evaluations affirm that the extension successfully neutralizes covert tracking vectors with minimal performance impact.

References

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Copyright

Copyright © 2026 Syed Bahauddin, V. Aruna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.