Date of Award

12-2022

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Interdisciplinary Informatics

First Advisor

Dr. William Mahoney

Abstract

Web browser fingerprinting is used to analyze client behavior through retrieval of browser attributes unique to the user’s browser, network and hardware profile. Third-party trackers are prevalent on the top Alexa sites and use JavaScript to retrieve and store user machine information in a stateless fashion. Stateless fingerprinting is performed through acquisition of client machine specifiers through an embedded JavaScript, which then forwards the information to a server. The client information is purportedly used to provide tailored advertising and enhance the browsing experience. However, the depth of captured client information often extends into the realm of personally identifiable information. The user is often unaware of privacy issues and how their information is disseminated for profit, or the risk of such data being used by hackers to exploit divulged vulnerabilities. We review fingerprinting techniques from previous works that delineate seminal methods and countermeasures, and present a novel fingerprinting JavaScript that measure over 200 Windows and Navigator object properties. The results reveal new parameters that can be used to generate unique user identifiers, and accurately track individual browsing behavior. These findings may be used by developers of anti-tracking software to improve efficacy and preserve individual privacy.

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