Quantifying Digital Footprints: a Client-Side Risk Scoring and Visualization

DOI

10.2991/978-94-6239-723-1_41How to use a DOI?

Keywords

Digital Footprint; Privacy Protection; Web Tracking; Data Visualization; Cookies; Browser Extension; User Analytics; Risk Assessment; Data Normalization; Fingerprinting Detection; Sankey Diagrams; Web Security; Online Privacy; Consent-Based Data Collection

Abstract

Digital footprints are the large amounts of user-specific digital traces that have built up because so many people use online services and third-party analytics. Companies can follow users across sites and make behavioural profiles using these traces, which include cookies, persistent web storage artifacts, network requests, and browser-level finger-printing signals. Most of the time, users don’t even know they’re being tracked [3], [8]. Most current privacy tools only stop one type of tracking at a time, and they don’t do a good job of letting you see how much tracking exposure you get on different websites [9]. This paper introduces a client-side quantitative framework for evaluating web tracking exposure via the comprehensive analysis of cookies, storage artifacts, and third party network interactions. We suggest a clear, rule-based privacy risk scoring model that takes into account things like identifier persistence, third-party prevalence, and security attributes. Comparing how users track their behavior across various websites is made possible by the framework, which transforms low-level browser telemetry into intelligible measurements and visualisations. An empirical analysis conducted on a diverse collection of real-world websites shows notable differences in tracking intensity between browsing categories, supporting the effectiveness of the suggested metrics in distinguishing between high- and low-risk environments. Since all analysis is completed within the user’s browser, no data must be transferred to other locations, protecting their privacy. The proposed method focuses on measurement and interpretability instead of blocking trackers. This gives users and researchers useful information about their digital footprint exposure while still protecting their privacy.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Vasudha Phaltankar
AU  - Vidya Harkal
AU  - Sankalp Mali
AU  - Sarvesh Kuvalekar
AU  - Aniket Jaitkar
AU  - Tanmay Malkar
PY  - 2026
DA  - 2026/07/14
TI  - Quantifying Digital Footprints: a Client-Side Risk Scoring and Visualization
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 463
EP  - 472
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_41
DO  - 10.2991/978-94-6239-723-1_41
ID  - Phaltankar2026
ER  -