Dataset: A Large-Scale Study of Cookie Banner Interaction Tools and their Impact on Users' Privacy / Part1

DOI

Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner's pervasiveness as they interrupt ``natural'' browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners.

In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for cookie banner interaction.'' We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user's preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set---even for extensions that aim to implement the same cookie choice. Extensions forcookie banner interaction'' can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.

This repository hosts the dataset corresponding to the paper "A Large-Scale Study of Cookie Banner Interaction Tools and their Impact on Users’ Privacy", which was published at the Privacy Enhancing Technologies Symposium (PETS) in 2024.

Identifier
DOI https://doi.org/10.35097/1708
Metadata Access https://www.radar-service.eu/oai/OAIHandler?verb=GetRecord&metadataPrefix=datacite&identifier=10.35097/1708
Provenance
Creator Demir, Nurullah ORCID logo; Urban, Tobias; Pohlmann, Norbert; Wressnegger, Christian
Publisher Karlsruhe Institute of Technology
Contributor RADAR
Publication Year 2023
Rights Open Access; Creative Commons Attribution 4.0 International; info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Representation
Resource Type Dataset
Format application/x-tar
Discipline Computer Science; Computer Science, Electrical and System Engineering; Engineering Sciences