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Lower Bounds for Private Graph Optimization Problems using Reconstruction Attacks
Jacob Imola, Rasmus Pagh, Lukas Retschmeier
This paper studies fundamental graph optimization problems under differential privacy (DP) and shows new, reconstruction-based lower bounds. We consider a graph $G = (V, E, \vec{w}…
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…
Privacy by Postprocessing the Discrete Laplace Mechanism
Quentin Hillebrand, Jacob Imola, Rasmus Pagh +1
We show that an "old dog", the classical discrete Laplace (aka.~geometric) mechanism, can "perform new tricks": 1. It can be post-processed to yield a simple, unbiased estimator of…
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…