Protecting Users Identity Against Browser Fingerprinting

Shingote, Prem Shankar (2022) Protecting Users Identity Against Browser Fingerprinting. Masters thesis, Dublin, National College of Ireland.

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Abstract

Nowadays, customer data is the gold mine for advertisers, marketing companies, and hackers. They use every possible method to track users' online activity, and currently they are using a new user-tracking mechanism called "Browser Fingerprinting." This method is different from cookie-based tracking; The browser fingerprinting mechanism collects common attributes of users' devices like OS version, screen resolution, font, and many more without their knowledge and combines them to generate one unique identifier token. This token helps attackers and advertisers spot that user over the internet with 90–99% accuracy. Consequently, the "privacy" of online users is seriously threatened; also, attackers can easily craft the attack based on the users' system configuration. After understanding the seriousness of the issue, modern browsers like Firefox, Brave, and TOR started blocking JavaScript. As a result, they are preventing users from browser fingerprinting, but due to the unavailability of JavaScript, many websites are not functioning properly. That’s why it’s challenging for users to protect their privacy. To resolve this privacy issue, we developed a browser extension called "Browser Fingerprint Defender," which anonymizes the users' browser by performing an API normalization against passive fingerprinting and object-based JavaScript fingerprinting. It masks the actual system values with generic random values, before sending them to the requested website. After randomizing the parameters, fingerprinting token values also change, and it becomes challenging for advertisers to track users online. To examine the effectiveness of our extension, we tested it on online experimental sites. And as per the test results, it provides appropriate anonymity to users and solves the problem of users' online privacy by making their identity less unique.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Ayala-Rivera, Vanessa UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	05 May 2023 11:48
Last Modified:	05 May 2023 11:48
URI:	https://norma.ncirl.ie/id/eprint/6544

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