Criteria and Analysis for Human-Centered Browser Fingerprinting Countermeasures Andalibi, Vafa Sadeqi Azer, Erfan Camp, L. Jean 2021-12-24T18:29:36Z 2021-12-24T18:29:36Z 2022-01-04
dc.description.abstract Browser fingerprinting is a surveillance technique that uses browser and device attributes to track visitors across the web. Defeating fingerprinting requires blocking attribute information or spoofing attributes, which can result in loss of functionality. To address the challenge of escaping surveillance while obtaining functionality, we identify six design criteria for an ideal spoofing system. We present three fingerprint generation algorithms as well as a baseline algorithm that simply samples a dataset of fingerprints. For each algorithm, we identify trade-offs among the criteria: distinguishability from a non-spoofed fingerprint, uniqueness, size of the anonymity set, efficient generation, loss of web functionality, and whether or not the algorithm protects the confidentiality of the underlying dataset. We report on a series of experiments illustrating that the use of our partially-dependent algorithm for spoofing fingerprints will avoid detection by Machine Learning approaches to surveillance.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.915
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Cyber Systems: Their Science, Engineering, and Security
dc.subject browser fingerprinting
dc.subject client fingerprinting
dc.subject end-user privacy protection
dc.subject fingerprinting defenses
dc.subject internet privacy
dc.title Criteria and Analysis for Human-Centered Browser Fingerprinting Countermeasures
dc.type.dcmi text
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
511.24 KB
Adobe Portable Document Format