BlUrM(or)e

BlUrM(or)e: Revisiting gender obfuscation in the user-item matrix

Strucks, Christopher (Radboud Universiteit Nijmegen)
Slokom, M. (TU Delft Multimedia Computing)
Larson, M.A. (TU Delft Multimedia Computing; Radboud Universiteit Nijmegen)

Burke, Robin (editor)
Abdollahpouri, Himan (editor)
Malthouse, Edward (editor)
Thai, KP (editor)
Zhang, Yongfeny (editor)

2019

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.

Data Obfuscation
Privacy
Recommender Systems

http://resolver.tudelft.nl/uuid:34269aa3-cc62-49c2-bafd-0925c2e37f96

CEUR-WS

RMSE 2019 Workshop on Recommendation in Multi-stakeholder Environments: Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019

2019 Workshop on Recommendation in Multi-Stakeholder Environments, RMSE 2019, 2019-09-20, Copenhagen, Denmark

CEUR Workshop Proceedings, 1613-0073, 2440

Institutional Repository

conference paper

© 2019 Christopher Strucks, M. Slokom, M.A. Larson