34 citations · 63 across the 26 of their papers we have counts for
6 papers · 1 filter
Metric geometry of the privacy-utility tradeoff
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Synthetic data are an attractive concept to enable privacy in data sharing. A fundamental question is how similar the privacy-preserving synthetic data are compared to the true dat…
Certified private data release for sparse Lipschitz functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal +3
As machine learning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning ques…
Private measures, random walks, and synthetic data
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Differential privacy is a mathematical concept that provides an information-theoretic security guarantee. While differential privacy has emerged as a de facto standard for guarante…
Private sampling: a noiseless approach for generating differentially private synthetic data
March Boedihardjo, Thomas Strohmer, Roman Vershynin
In a world where artificial intelligence and data science become omnipresent, data sharing is increasingly locking horns with data-privacy concerns. Differential privacy has emerge…
Privacy of synthetic data: a statistical framework
March Boedihardjo, Thomas Strohmer, Roman Vershynin
Privacy-preserving data analysis is emerging as a challenging problem with far-reaching impact. In particular, synthetic data are a promising concept toward solving the aporetic co…
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data
March Boedihardjo, Thomas Strohmer, Roman Vershynin
The protection of private information is of vital importance in data-driven research, business, and government. The conflict between privacy and utility has triggered intensive res…