How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users
Authors: Alex Berke (MIT Media Lab), Badih Ghazi (Google), Enrico Bacis (Google), Pritish Kamath (Google), Ravi Kumar (Google), Robin Lassonde (Google), Pasin Manurangsi (Google), Umar Syed (Google)
Volume: 2025
Issue: 1
Pages: 720–758
DOI: https://doi.org/10.56553/popets-2025-0038
Abstract: Browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique 'fingerprints'. This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies used data not publicly available. Additionally, data in prior studies lacked user demographics. Here we provide a first-of-its-kind dataset to enable further research. It includes browser attributes with users' demographics and survey responses, collected with informed consent from 8,400 US study participants. We use this dataset to demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users' age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk. Our data collection process also conducted an experiment to study what impacts users' likelihood to share browser data for open research, in order to inform future data collection efforts, with responses from 12,461 total participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect. Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we provide can be used to further study research questions not addressed in this work.
Keywords: browser fingerprinting, tracking, privacy, open data
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