Applying Differential Privacy to Matrix Factorization

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Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, methodology): “B[1] = B[2] = · · · = B[T ] C[1] = C[2] = · · · = C[T ].”
Secure Federated Matrix Factorization (2019, unknown): “…during MF. Prior studies have studied privacy-preserving MF in two main types: (1) Obfuscation-based methods obfuscate users’ preference raw data before releasing it to the central server so as to ensure certain level of privacy protection (e.g., differential privacy) [ Berlioz et al. , 2015 ] .”
A Simple and Efficient Federated Recommender System (2019, methodology): “Among the extensive research to secure privacy in recommender systems are methods that include cryptography algorithms [7, 23] and differential privacy [4, 20].”
Towards a More Reliable Privacy-preserving Recommender System (2017, methodology): “The (cid:15) -diﬀerential privacy of adding Laplacian noises to ratings has been proved in [8].”