Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Fueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code...

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Bibliographic Details
Main Authors: Rizzo Valentino, Traverso Stefano, Mellia Marco
Format: Article
Language:English
Published: Sciendo 2021-01-01
Series:Proceedings on Privacy Enhancing Technologies
Subjects:
tracking
fingerprinting
machine learning
static code analysis
dynamic code analysis
Online Access:https://doi.org/10.2478/popets-2021-0004
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spelling doaj-d1bd610231244adbbe2bb4a44075dcce2021-09-05T14:01:11ZengSciendoProceedings on Privacy Enhancing Technologies2299-09842021-01-0120211436310.2478/popets-2021-0004popets-2021-0004Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine LearningRizzo Valentino0Traverso Stefano1Mellia Marco2Ermes Cyber Security S.R.L., Turin, ItalyErmes Cyber Security S.R.L., Turin, ItalyPolitecnico di Torino & Ermes Cyber Security S.R.L., Turin, ItalyFueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters.https://doi.org/10.2478/popets-2021-0004trackingfingerprintingmachine learningstatic code analysisdynamic code analysis
collection DOAJ
language English
format Article
sources DOAJ
author Rizzo Valentino
Traverso Stefano
Mellia Marco
spellingShingle Rizzo Valentino
Traverso Stefano
Mellia Marco
Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
Proceedings on Privacy Enhancing Technologies
tracking
fingerprinting
machine learning
static code analysis
dynamic code analysis
author_facet Rizzo Valentino
Traverso Stefano
Mellia Marco
author_sort Rizzo Valentino
title Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
title_short Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
title_full Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
title_fullStr Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
title_full_unstemmed Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
title_sort unveiling web fingerprinting in the wild via code mining and machine learning
publisher Sciendo
series Proceedings on Privacy Enhancing Technologies
issn 2299-0984
publishDate 2021-01-01
description Fueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters.
topic tracking
fingerprinting
machine learning
static code analysis
dynamic code analysis
url https://doi.org/10.2478/popets-2021-0004
work_keys_str_mv AT rizzovalentino unveilingwebfingerprintinginthewildviacodeminingandmachinelearning
AT traversostefano unveilingwebfingerprintinginthewildviacodeminingandmachinelearning
AT melliamarco unveilingwebfingerprintinginthewildviacodeminingandmachinelearning
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