Dr. Hafiz Imtiaz

Recommendation systems use a lot of user data, which can risk exposing sensitive personal information. Differential privacy can protect users, but when applied to neural‑network‑based recommenders, it often reduces accuracy—creating a trade‑off between privacy and performance. Traditional matrix‑factorization ethods struggle to balance this trade‑off. In this work, we propose a neural‑network‑based collaborative filtering model that achieves both strong privacy and high accuracy. Using Rényi differential privacy, we measure how much privacy is lost during training and test the model on real datasets under different privacy settings. Our results show that the proposed ANN model provides strong privacy guarantees while maintaining excellent recommendation quality, outperforming many existing private and non‑private methods. This work led to this Measurement publication.