23 citations · 38 across the 8 of their papers we have counts for
Showing 2024 · cs.LGShow all
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cs.LG2024
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
Xinwei Zhang, Zhiqi Bu, Borja Balle +3
Differential privacy (DP) offers a robust framework for safeguarding individual data privacy. To utilize DP in training modern machine learning models, differentially private optim…
cs.LG2024
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
Xinwei Zhang, Zhiqi Bu, Mingyi Hong +1
Privacy is a growing concern in modern deep-learning systems and applications. Differentially private (DP) training prevents the leakage of sensitive information in the collected t…
cs.LG2024
Pre-training Differentially Private Models with Limited Public Data
Zhiqi Bu, Xinwei Zhang, Mingyi Hong +2
The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that…