13 papers
Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization
Pavel Arkhipov, Nikita P. Kalinin
Continual counting under pure differential privacy is one of the simplest and most well-studied problems in the continual observation model. Nevertheless, an asymptotic gap remains…
Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy
Nikita P. Kalinin, Rasmus Pagh
We present the dithered Gaussian mechanism, a novel alternative to the discrete Gaussian mechanism for differential privacy that discretizes the private output rather than the nois…
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
Nikita P. Kalinin, Aki Rehn, Joel Daniel Andersson +2
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, an…
Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
Puyu Wang, Jan Schuchardt, Nikita Kalinin +4
We establish the first population risk bounds for Kolmogorov-Arnold Networks (KANs) trained by mini-batch SGD with gradient clipping, covering non-private SGD as well as differenti…
DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
Nikita P. Kalinin, Ryan McKenna, Rasmus Pagh +1
Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees. Several recent ex…
Matrix Factorization for Practical Continual Mean Estimation Under User-Level Differential Privacy
Nikita P. Kalinin, Ali Najar, Valentin Roth +1
We study continual mean estimation, where data vectors arrive sequentially and the goal is to maintain accurate estimates of the running mean. We address this problem under user-le…