Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization
This reveals a privacy threat for users who withhold demographic data, showing incremental improvements in attack efficiency.
The paper tackles the problem of recommender systems learning private user attributes without disclosure by designing passive and active attacks using Bayesian matrix factorization, achieving feasibility with significantly fewer rated items than static methods while maintaining recommendation quality.
Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that solicit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.