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cs.CR2026
Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy
Nikita P. Kalinin, Rasmus Pagh
We present the dithered Gaussian mechanism, an alternative to the discrete Gaussian mechanism for differential privacy that discretizes the private output rather than the noise dis…
cs.CR2025★ 1 cited
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
Nikita P. Kalinin, Ryan McKenna, Jalaj Upadhyay +1
Matrix factorization mechanisms for differentially private training have emerged as a promising approach to improve model utility under privacy constraints. In practical settings,…