{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:19:41Z","timestamp":1783009181283,"version":"3.54.5"},"reference-count":116,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"3","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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\u2014canvas 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.<\/jats:p>","DOI":"10.2478\/popets-2021-0056","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T02:06:59Z","timestamp":1619662019000},"page":"453-473","source":"Crossref","is-referenced-by-count":9,"title":["ML-CB: Machine Learning Canvas Block"],"prefix":"10.56553","volume":"2021","author":[{"given":"Nathan","family":"Reitinger","sequence":"first","affiliation":[{"name":"University of Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle L.","family":"Mazurek","sequence":"additional","affiliation":[{"name":"University of Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"35752","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"2022050204481473528_j_popets-2021-0056_ref_001_w2aab3b7c46b1b6b1ab1ab1Aa","unstructured":"[1] E. Zuckerman, \u201cThe internet\u2019s original sin,\u201d The Atlantic, vol. 14, August 2014. 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