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02/19/2023
Sample-efficient private data release for Lipschitz functions under sparsity assumptions
Differential privacy is the de facto standard for protecting privacy in ...
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01/06/2023
Covariance loss, Szemeredi regularity, and differential privacy
We show how randomized rounding based on Grothendieck's identity can be ...
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04/20/2022
Private measures, random walks, and synthetic data
Differential privacy is a mathematical concept that provides an informat...
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09/30/2021
Private sampling: a noiseless approach for generating differentially private synthetic data
In a world where artificial intelligence and data science become omnipre...
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09/03/2021
Privacy of synthetic data: a statistical framework
Privacy-preserving data analysis is emerging as a challenging problem wi...
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07/13/2021
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data
The protection of private information is of vital importance in data-dri...
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07/10/2020
A Performance Guarantee for Spectral Clustering
The two-step spectral clustering method, which consists of the Laplacian...
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06/28/2019
