Synthetic data–anonymisation groundhog day
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data
publishing that addresses the shortcomings of traditional anonymisation techniques. The …
publishing that addresses the shortcomings of traditional anonymisation techniques. The …
Benchmarking differentially private synthetic data generation algorithms
This work presents a systematic benchmark of differentially private synthetic data generation
algorithms that can generate tabular data. Utility of the synthetic data is evaluated by …
algorithms that can generate tabular data. Utility of the synthetic data is evaluated by …
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …
[HTML][HTML] Can I trust my fake data–A comprehensive quality assessment framework for synthetic tabular data in healthcare
VB Vallevik, A Babic, SE Marshall, E Severin… - International Journal of …, 2024 - Elsevier
Background Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient
data for training, testing and validation. Synthetic data has been suggested in response to …
data for training, testing and validation. Synthetic data has been suggested in response to …
Differentially private synthetic data: Applied evaluations and enhancements
Machine learning practitioners frequently seek to leverage the most informative available
data, without violating the data owner's privacy, when building predictive models …
data, without violating the data owner's privacy, when building predictive models …
Synthetic data for privacy-preserving clinical risk prediction
Synthetic data promise privacy-preserving data sharing for healthcare research and
development. Compared with other privacy-enhancing approaches—such as federated …
development. Compared with other privacy-enhancing approaches—such as federated …
Comparative study of differentially private synthetic data algorithms from the NIST PSCR differential privacy synthetic data challenge
Differentially private synthetic data generation offers a recent solution to release analytically
useful data while preserving the privacy of individuals in the data. In order to utilize these …
useful data while preserving the privacy of individuals in the data. In order to utilize these …
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
Generative models trained with Differential Privacy (DP) can produce synthetic data while
reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the …
reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding the …
Towards principled assessment of tabular data synthesis algorithms
Data synthesis has been advocated as an important approach for utilizing data while
protecting data privacy. A large number of tabular data synthesis algorithms (which we call …
protecting data privacy. A large number of tabular data synthesis algorithms (which we call …
30 years of synthetic data
The idea to generate synthetic data as a tool for broadening access to sensitive microdata
has been proposed for the first time three decades ago. While first applications of the idea …
has been proposed for the first time three decades ago. While first applications of the idea …