Web: http://arxiv.org/abs/2206.07910

June 17, 2022, 1:10 a.m. | R Adithya Gowtham, Gokularam M, Thulasi Tholeti, Sheetal Kalyani

cs.LG updates on arXiv.org arxiv.org

Performing low-rank matrix completion with sensitive user data calls for
privacy-preserving approaches. In this work, we propose a novel noise addition
mechanism for preserving differential privacy where the noise distribution is
inspired by Huber loss, a well-known loss function in robust statistics. The
proposed Huber mechanism is evaluated against existing differential privacy
mechanisms while solving the matrix completion problem using the Alternating
Least Squares approach. We also propose using the Iteratively Re-Weighted Least
Squares algorithm to complete low-rank matrices and …


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