On Analyzing Third-party Tracking via Machine Learning

Alfonso Guarino, Delfina Malandrino, Rocco Zaccagnino, Federico Cozza, Antonio Rapuano

2020

Abstract

Nowadays, websites rely on services provided by third party sites to track users and offer personalized experiences. However, this practice threatens the privacy of individuals through the use of valuable information to create a digital personal profile. The existing client-side countermeasures to protect privacy, exhibit performance issues, mainly due to the use of blacklisting mechanisms (list of resources to be filtered out). In this paper, we study the use of machine learning methods to classify the thirdy-party privacy intrusive resources (trackers). To this end, we first downloaded (browsing Alexa’s Top 10 websites for each category like sports, shopping etc.) a dataset of 1000 web resources split into functional and tracking, and then we identified suitable metrics to distinguish between the two classes. In order to evaluate the effectiveness of the proposed metrics we have compared the performances of several machine learning models based on supervised learning among the most used in literature. As a result, we obtained that the Random Forest can classify functional and tracking resources with an accuracy of 91%.

Paper Citation

in Harvard Style

Guarino A., Malandrino D., Zaccagnino R., Cozza F. and Rapuano A. (2020). On Analyzing Third-party Tracking via Machine Learning. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-399-5, pages 532-539. DOI: 10.5220/0008972005320539

in Bibtex Style

@conference{icissp20,
author={Alfonso Guarino and Delfina Malandrino and Rocco Zaccagnino and Federico Cozza and Antonio Rapuano},
title={On Analyzing Third-party Tracking via Machine Learning},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2020},
pages={532-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008972005320539},
isbn={978-989-758-399-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - On Analyzing Third-party Tracking via Machine Learning
SN - 978-989-758-399-5
AU - Guarino A.
AU - Malandrino D.
AU - Zaccagnino R.
AU - Cozza F.
AU - Rapuano A.
PY - 2020
SP - 532
EP - 539
DO - 10.5220/0008972005320539