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Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
[article]
2021
arXiv
pre-print
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) the best known privacy/utility trade-off both theoretically, as well as on benchmark data sets. In particular, we provide the first global convergence analysis of ALS with noise
arXiv:2107.09802v1
fatcat:ao7wdfub45bpxow736paoy72wm