Stronger privacy for federated collaborative filtering with implicit feedback - Minto,L; Haller,M; Haddadi,H; Livshits,B (ACM, 2021)
Recommender systems are commonly trained on centrally collected userinteraction data like views or clicks. This practice however raises seriousprivacy concerns regarding the recommender's collection and handling ofpotentially sensitive data. Several privacy-aware recommender systems have beenproposed in recent literature, but comparatively little attention has beengiven to systems at the intersection of implicit feedback and privacy. Toaddress this shortcoming, we propose a practical federated recommender systemfor implicit data under user-level local differential privacy (LDP). Theprivacy-utility trade-off is controlled by parameters $ε$ and $k$,regulating the per-update privacy budget and the number of $ε$-LDPgradient updates sent by each user respectively. To further protect the user'sprivacy, we introduce a proxy network to reduce the fingerprinting surface byanonymizing and shuffling the reports before forwarding them to therecommender. We empirically demonstrate the effectiveness of our framework onthe MovieLens dataset, achieving up to Hit Ratio with K=10 (HR@10) 0.68 on 50kusers with 5k items. Even on the full dataset, we show that it is possible toachieve reasonable utility with HR@10>0.5 without compromising user privacy.