Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access. - Martin Aumüller 001; Christian Janos Lebeda; Rasmus Pagh (CCS, 2021)

Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access. - Christian Janos Lebeda; Martin Aumüller 001; Rasmus Pagh (J. Priv. Confidentiality, 2022)

Correlated-Output-Differential-Privacy and Applications to Dark Pools. - James Hsin-yu Chiang; Bernardo David; Mariana Gama; Christian Janos Lebeda (IACR Cryptol. ePrint Arch., 2023)

PLAN: Variance-Aware Private Mean Estimation. - Martin Aumüller 001; Christian Janos Lebeda; Boel Nelson; Rasmus Pagh (Proc. Priv. Enhancing Technol., 2024)

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch. - Christian Janos Lebeda; Jakub Tetek (SIGMOD Rec., 2024)

The Correlated Gaussian Sparse Histogram Mechanism. - Christian Janos Lebeda; Lukas Retschmeier (FORC, 2025)

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition. - Christian Janos Lebeda; Matthew Regehr; Gautam Kamath 001; Thomas Steinke 002 (SaTML, 2025)

Better Gaussian Mechanism using Correlated Noise. - Christian Janos Lebeda (SOSA, 2025)

Testing Identity of Distributions under Kolmogorov Distance in Polylogarithmic Space. - Christian Janos Lebeda; Jakub Tetek (SOSA, 2025)

Model Agnostic Differentially Private Causal Inference. - Christian Janos Lebeda; Mathieu Even; Aurélien Bellet; Julie Josse (CoRR, 2025)