Differentially Private Matrix Factorization

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Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, methodology): “[16] proposed an objective perturbation method for matrix factorization in recommendation systems.”
Towards a More Reliable Privacy-preserving Recommender System (2017, background): “Still, it requires a third-party , i.e., an additional server between clients and the server of the service provider, as its noise sampling method is vulnerable to diﬀerence attack deﬁned by [7].”
Practical Privacy Preserving POI Recommendation (2020, methodology): “r privacy while building recommender systems, including the applications to POI recommendation [11, 47]. They mainly belong to two types. The first type protects the raw data by adding noises to them [4, 26, 37, 38, 44, 47]. These methods are efficient and easy to implement, however, the recommendat”