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cs.CR2023
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…
cs.CR2021
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…