23 citations · 26 across the 5 of their papers we have counts for
5 papers
Towards Better Attribute Inference Vulnerability Measures
Paul Francis, David Wagner
The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. An important class of attack…
A Consensus Privacy Metrics Framework for Synthetic Data
Lisa Pilgram, Fida K. Dankar, Jorg Drechsler +12
Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy…
A Comparison of SynDiffix Multi-table versus Single-table Synthetic Data
Paul Francis
SynDiffix is a new open-source tool for structured data synthesis. It has anonymization features that allow it to generate multiple synthetic tables while maintaining strong anonym…
Towards more accurate and useful data anonymity vulnerability measures
Paul Francis, David Wagner
The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. There is a large body of wor…
SynDiffix: More accurate synthetic structured data
Paul Francis, Cristian Berneanu, Edon Gashi
This paper introduces SynDiffix, a mechanism for generating statistically accurate, anonymous synthetic data for structured data. Recent open source and commercial systems use Gene…