1 citations · 1 across the 7 of their papers we have counts for
9 papers
Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
Christian Janos Lebeda, David Erb, Tudor Cebere +1
Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point…
Weighted Fourier Factorizations: Optimal Gaussian Noise for Differentially Private Marginal and Product Queries
Christian Janos Lebeda, Aleksandar Nikolov, Haohua Tang
We revisit the task of releasing marginal queries under differential privacy with additive (correlated) Gaussian noise. We first give a construction for answering arbitrary workloa…
The Correlated Gaussian Sparse Histogram Mechanism
Christian Janos Lebeda, Lukas Retschmeier
We consider the problem of releasing a sparse histogram under -differential privacy. The stability histogram independently adds noise from a Laplace or Gaussian d…
Testing Identity of Distributions under Kolmogorov Distance in Polylogarithmic Space
Christian Janos Lebeda, Jakub Tětek
Suppose we have a sample from a distribution and we want to test whether for a fixed distribution . Specifically, we want to reject with constant probability, if…
Better Gaussian Mechanism using Correlated Noise
Christian Janos Lebeda
We present a simple variant of the Gaussian mechanism for answering differentially private queries when the sensitivity space has a certain common structure. Our motivating problem…
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
Christian Janos Lebeda, Matthew Regehr, Gautam Kamath +1
We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the pri…
PLAN: Variance-Aware Private Mean Estimation
Martin Aumüller, Christian Janos Lebeda, Boel Nelson +1
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy…
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
Christian Janos Lebeda, Jakub Tětek
We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using t…
Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access
Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh
Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy. An ideal solution would use space close to information-theoretica…