Shared MF: A privacy-preserving recommendation system
This work addresses privacy concerns in distributed recommendation systems for e-commerce and online video platforms, though it appears incremental as it builds on federated learning and secret sharing techniques.
The paper tackles the privacy problem in multi-source data for distributed recommendation systems by proposing SharedMF, a privacy-preserving matrix factorization scheme based on federated learning and secret sharing. Experimental results show it achieves faster execution speed compared to existing homomorphic encryption methods without privacy disclosure, better adapting to large-scale recommendation scenarios.
Matrix factorization is one of the most commonly used technologies in recommendation system. With the promotion of recommendation system in e-commerce shopping, online video and other aspects, distributed recommendation system has been widely promoted, and the privacy problem of multi-source data becomes more and more important. Based on Federated learning technology, this paper proposes a shared matrix factorization scheme called SharedMF. Firstly, a distributed recommendation system is built, and then secret sharing technology is used to protect the privacy of local data. Experimental results show that compared with the existing homomorphic encryption methods, our method can have faster execution speed without privacy disclosure, and can better adapt to recommendation scenarios with large amount of data.