Evaluating Anti-Fingerprinting Privacy Enhancing Technologies

TitleEvaluating Anti-Fingerprinting Privacy Enhancing Technologies
Publication TypeConference Paper
Year of Publication2019
AuthorsDatta, A., Lu J., & Tschantz M. Carl
Published inProceedings of the WWW'19 (World Wide Web Conference)

We study how to evaluate Anti-Fingerprinting Privacy Enhancing Technologies (AFPETs). Experimental methods have the advantage of control and precision, and can be applied to new AFPETs that currently lack a user base. Observational methods have the advantage of scale and drawing from the browsers currently in real-world use. We propose a novel combination of these methods, offering the best of both worlds, by applying experimentally created models of a AFPET’s behavior to an observational dataset. We apply our evaluation methods to a collection of AFPETs to find the Tor Browser Bundle to be the most effective among them. We further uncover inconsistencies in some AFPETs’ behaviors.


We thank Pierre Laperdrix for providing us a dataset of real-world fingerprints from amiunique.org, and Milan Ganai for investigating how to automate the use of PETs on Windows. We thank Lay Kuan Loh and Zheng Zong for assistance in exploring the application of information flow experiments to evaluate PETs. We thank Anupam Datta for discussions about this work. We gratefully acknowledge funding support from the National Science Foundation (Grants 1514509 and 1704985). The opinions in this paper are those of the authors and do not necessarily reflect the opinions of any funding sponsor or the United States Government.

ICSI Research Group

Networking and Security