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M. Yassaee

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#machine learning Preprint Sep 2026

Geometry-Adaptive Mechanisms for Private Synthetic Data

Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of size $n$ on $[0,1]^d$ with $d\ge2$, existing pure $\varepsilon$-differentially private mechanisms achieve expected $1$-Wasserstein error of order $(\varepsilon n)^{-1/d}...

Raoof Zare Moayedi, Amir R. Asadi, M. Yassaee et al. · 0 citations