3 citations · 5 across the 14 of their papers we have counts for
16 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, an alternative to the discrete Gaussian mechanism for differential privacy that discretizes the private output rather than the noise dis…
Beyond Square Roots: A Memory-Efficient Explicit 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…
Optimization Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
Puyu Wang, Jan Schuchardt, Nikita Kalinin +5
The theoretical understanding of differentially private stochastic gradient descent (DP-SGD) with temporally correlated noise remains limited, particularly for non-convex neural ne…
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…
DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning
Mihaela Hudişteanu, Nikita P. Kalinin, Edwige Cyffers
Adaptive optimizers are the de facto standard in non-private training as they often enable faster convergence and improved performance. In contrast, differentially private (DP) tra…
Learning Rate Scheduling with Matrix Factorization for Private Training
Nikita P. Kalinin, Joel Daniel Andersson
We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via ma…
Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay
The factorization norms of the lower-triangular all-ones matrix, and , play a central role in differential privacy as they are used…
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,…
Binned Group Algebra Factorization for Differentially Private Continual Counting
Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay
We study memory-efficient matrix factorization for differentially private counting under continual observation. While recent work by Henzinger and Upadhyay 2024 introduced a factor…
Continual Release Moment Estimation with Differential Privacy
Nikita P. Kalinin, Jalaj Upadhyay, Christoph H. Lampert
We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of data with reduced noise compared to naive approache…
Notes on Sampled Gaussian Mechanism
Nikita P. Kalinin
In these notes, we prove a recent conjecture posed in the paper by Räisä, O. et al. [Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Opti…
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
Nikita P. Kalinin, Simone Bombari, Hossein Zakerinia +1
We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model…
Banded Square Root Matrix Factorization for Differentially Private Model Training
Nikita P. Kalinin, Christoph Lampert
Current state-of-the-art methods for differentially private model training are based on matrix factorization techniques. However, these methods suffer from high computational overh…
Efficient Estimation of a Gaussian Mean with Local Differential Privacy
Nikita P. Kalinin, Lukas Steinberger
In this paper we study the problem of estimating the unknown mean of a unit variance Gaussian distribution in a locally differentially private (LDP) way. In the high-privacy re…