Author

Rasmus Pagh

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#machine learning Jul 2026

Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy

The dithered Gaussian mechanism is presented, an alternative to the discrete Gaussian mechanism for differential privacy that discretizes the private output rather than the noise distribution itself, and it is shown that cryptographically secure noise generation with reduced exposure to floating-point vulnerabilities c...

Nikita P. Kalinin, Rasmus Pagh · arXiv.org · 1 citation

#machine learning Jan 2026

DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training

This work proposes a new algorithm for correlated-noise private training that correlates noise only with the immediately preceding iteration and cancels a tunable portion of it and shows that the computational overhead is minimal and improved accuracy over DP-SGD is demonstrated.

Nikita P. Kalinin, Ryan McKenna, Rasmus Pagh et al. · arXiv.org · 3 citations

Preprint Sep 2026

Lower Bounds for Private Graph Optimization Problems using Reconstruction Attacks

This paper studies fundamental graph optimization problems under differential privacy (DP) and shows new, reconstruction-based lower bounds for large families of sparse graphs with expansion properties and extends lower bounds of Deng et al. (ICLR~'25) to general graphs and to approximate DP.

Jacob Imola, Rasmus Pagh, Lukas Retschmeier · 1 citation