1. bookVolume 2021 (2021): Issue 3 (July 2021)
Journal Details
First Published
16 Apr 2015
Publication timeframe
4 times per year
access type Open Access

ML-CB: Machine Learning Canvas Block

Published Online: 27 Apr 2021
Page range: 453 - 473
Received: 30 Nov 2020
Accepted: 16 Mar 2021
Journal Details
First Published
16 Apr 2015
Publication timeframe
4 times per year

With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.


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