Disseminating microdata to the public that provide a high level of data utility, while at the same time guaranteeing the confidentiality of the survey respondent is a difficult task. Generating multiply imputed synthetic datasets is an innovative statistical disclosure limitation technique with...

"Questions on income in surveys are prone to two sources of errors that can cause bias if not addressed adequately at the analysis stage. On the one hand, income is considered sensitive information and response rates on income questions generally tend to be lower than response rates for other...

When intense redaction is needed to protect the confidentiality of data subjects' identities and sensitive attributes, statistical agencies can use synthetic data approaches. To create synthetic data, the agency replaces identifying or sensitive values with draws from statistical models...

"The basic concept of multiple imputation is straightforward and easy to understand, but the application to real data imposes many implementation problems. To define useful imputation models for a dataset that consists of categorical and of continuous variables with distributions that are...

"For micro-datasets considered for release as scientific or public use files, statistical agencies have to face the dilemma of guaranteeing the confidentiality of survey respondents on the one hand and offering sufficiently detailed data on the other hand. For that reason a variety of methods to...

"To protect the cofidentiality of survey respondents' identities and sensitive attributes, statistical agencies can release data in which cofidential values are replaced with multiple imputations. These are called synthetic data. We propose a two-stage approach to generating synthetic data that...