In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example
This book on statistical disclosure control presents the theory, applications and software implement...
KeynoteThe demand for and volume of data from surveys, registers or other sources containing sensibl...
Over the past three decades, synthetic data methods for statistical disclosure control have continua...
In many contexts, confidentiality constraints severely restrict access to unique and valuable microd...
The production of synthetic datasets has been proposed as a statistical disclosure control solution ...
The production of synthetic datasets has been proposed as a statistical disclosure control solution ...
Open research data provide considerable scientific, societal, and economic benefits. However, disclo...
Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive informa...
Abstract: To protect the confidentiality of survey respondents ’ identities and sensi-tive attribute...
The analysis of large administrative data sets can provide researchers with answers to many research...
Synthetic datasets simultaneously allow for the dissemination of research data while protecting the ...
Archive for the synthetic data pre-conference workshop at the Open Science Festival on September 1, ...
Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that e...
Synthetic data generation is a powerful tool for privacy protection when considering public release ...
This book on statistical disclosure control presents the theory, applications and software implement...
This book on statistical disclosure control presents the theory, applications and software implement...
KeynoteThe demand for and volume of data from surveys, registers or other sources containing sensibl...
Over the past three decades, synthetic data methods for statistical disclosure control have continua...
In many contexts, confidentiality constraints severely restrict access to unique and valuable microd...
The production of synthetic datasets has been proposed as a statistical disclosure control solution ...
The production of synthetic datasets has been proposed as a statistical disclosure control solution ...
Open research data provide considerable scientific, societal, and economic benefits. However, disclo...
Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive informa...
Abstract: To protect the confidentiality of survey respondents ’ identities and sensi-tive attribute...
The analysis of large administrative data sets can provide researchers with answers to many research...
Synthetic datasets simultaneously allow for the dissemination of research data while protecting the ...
Archive for the synthetic data pre-conference workshop at the Open Science Festival on September 1, ...
Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that e...
Synthetic data generation is a powerful tool for privacy protection when considering public release ...
This book on statistical disclosure control presents the theory, applications and software implement...
This book on statistical disclosure control presents the theory, applications and software implement...
KeynoteThe demand for and volume of data from surveys, registers or other sources containing sensibl...
Over the past three decades, synthetic data methods for statistical disclosure control have continua...