BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems

Hou, Dongkun; Zhang, Jie

doi:10.1109/bigdata59044.2023.10386918

Cited by 1 publication

(1 citation statement)

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“…Early recommendation systems relied heavily on collaborative filtering techniques [3,4], which make predictions about the interests of a user by collecting preferences from many users [3]. This approach was further refined through matrix factorization techniques, which decompose the user-item interaction matrix into lower-dimensional matrices, capturing latent factors associated with users and items [5,6].…”

Section: Recommendation Systemmentioning

confidence: 99%

An Effective Federated Recommendation Framework with Differential Privacy

Xu,

Chu,

Song

2024

Electronics

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This paper proposes a novel federated recommendation framework that incorporates differential privacy to safeguard user privacy without compromising on the accuracy of recommendations. Unlike conventional recommendation systems that centralize user data, leading to potential privacy breaches, our framework ensures that user data remain on local devices. It leverages a federated learning approach, where a global model is trained across multiple devices without exchanging raw data. To enhance privacy protection, we integrate a specially designed differential privacy algorithm that adds carefully calibrated noise to the aggregated data updates. This mechanism ensures that the global model cannot be exploited to infer individual user information. We evaluate our framework on two real-world datasets, one from the e-commerce sector and another from the multimedia content recommendation domain. The results exhibit that our framework achieves competitive recommendation accuracy compared to traditional centralized approaches, with minimal loss in precision and recall metrics, while significantly enhancing user privacy. Our work stands as a testament to the feasibility of creating recommendation systems that do not have to choose between privacy and performance, paving the way for more ethical AI applications in sensitive domains.

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