Yu-Xiang Wang

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20232026

most citedImproving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners

1 citations · 2 across the 4 of their papers we have counts for

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cs.CR2025

Purifying Approximate Differential Privacy with Randomized Post-processing

Yingyu Lin, Erchi Wang, Yi-An Ma +1

We propose a framework to convert $(ε, δ)$ -approximate Differential Privacy (DP) mechanisms into $(ε^{'}, 0)$ -pure DP mechanisms under certain conditions, a proces…

cs.CR2024

Privacy Profiles for Private Selection

Antti Koskela, Rachel Redberg, Yu-Xiang Wang

Private selection mechanisms (e.g., Report Noisy Max, Sparse Vector) are fundamental primitives of differentially private (DP) data analysis with wide applications to private query…