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Found 2 papers, 1 papers with code
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
In this study, we show that these two types of noise are equivalent in their effect on the utility of private neural networks, however they are not accounted for equally in the privacy budget.
Super-convergence and Differential Privacy: Training faster with better privacy guarantees
The combination of deep neural networks and Differential Privacy has been of increasing interest in recent years, as it offers important data protection guarantees to the individuals of the training datasets used.
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