Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. (arXiv:2211.06530v1 [cs.LG])

We introduce new differentially private (DP) mechanisms for gradient-based
machine learning (ML) training involving multiple passes (epochs) of a dataset,
substantially improving the achievable privacy-utility-computation tradeoffs.
Our key contribution is an extension of the online matrix factorization DP
mechanism to multiple participations, substantially generalizing the approach
of DMRST2022. We first give conditions under which it is possible to reduce the
problem with per-iteration vector contributions to the simpler one of scalar
contributions. Using this, we formulate the construction of optimal …

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