Privacy of synthetic data: a statistical framework

Privacy of synthetic data: a statistical framework

3 September 2021

Thomas Strohmer

Roman Vershynin

Papers citing "Privacy of synthetic data: a statistical framework"

6 / 6 papers shown

Title
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review Sonal Allana Mohan Kankanhalli Rozita Dara 32 0 0 05 May 2025
Optimizing Mouse Dynamics for User Authentication by Machine Learning: Addressing Data Sufficiency, Accuracy-Practicality Trade-off, and Model Performance Challenges Yi Wang Chengyv Wu Yang Liao Maowei You AAML 39 0 0 30 Apr 2025
Certified private data release for sparse Lipschitz functions Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 46 3 0 19 Feb 2023
Private sampling: a noiseless approach for generating differentially private synthetic data M. Boedihardjo Thomas Strohmer Roman Vershynin SyDa 29 14 0 30 Sep 2021
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data M. Boedihardjo Thomas Strohmer Roman Vershynin 42 23 0 13 Jul 2021
Leveraging Public Data for Practical Private Query Release Terrance Liu G. Vietri Thomas Steinke Jonathan R. Ullman Zhiwei Steven Wu 161 58 0 17 Feb 2021