Year: 2019
Type: conference-paper
Abstract: Past research has demonstrated that removing implicit gender information from the user-item matrix does not result in substantial performance losses.Such results point towards promising solutions for protecting users' privacy without compromising prediction performance, which are of particular interest in multistakeholder environments.Here, we investigate BlurMe, a gender obfuscation technique that has been shown to block classifiers from inferring binary gender from users' profiles.We first point out a serious shortcoming of BlurMe: Simple data visualizations can reveal that BlurMe has been applied to a data set, including which items have been impacted.We then propose an extension to BlurMe, called BlurM(or)e, that addresses this issue.We reproduce the original BlurMe experiments with the MovieLens data set, and point out the relative advantages of BlurM(or)e.
Institutions: Delft University of Technology, Radboud University Nijmegen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Gender equality
Open Access status: green
OpenAlex ID: W2977748183