A Framework for Model Inversion Attacks on Collaborative Filtering
Recommender systems using collaborative filtering have become increasingly popular in recent years. Such system predicts items that users would prefer based on their ratings (or purchase history) Data poisoning attack on collaborative filtering has been introduced by Li et al. in 2016, for the purpose of degrading the performance of a recommender system, or boosting/reducing the popularity of specific items. In this paper, we use data poisoning to expose user privacy and propose a model inversion attack on collaborative filtering. Our proposed attack enables to infer the sensitive items that a user rated highly (or purchased) from a recommended item.