Information preserving regression-based tools for statistical disclosure control

Distributed Privacy-Preserving Methods for Statistical Disclosure Control

Distribution-preserving statistical disclosure limitation

One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with con…dential data replaced by multiply-imputed synthetic values. A mis-speci…ed imputation model can invalidate inferences because the distribution of synthetic data is completely determined by the model used to generate th...

Semantic-based aggregation for statistical disclosure control

In recent years, the so-called information explosion has caused the development of new techniques for data analysis and information management. One class of techniques where this improvement can be found is the one related with information fusion and knowledge integration. As the number of available information sources and the amounts of information increase, the need for these techniques also ...

Distribution-Preserving Statistical Disclosure Limitation1

One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with con…dential data replaced by multiply-imputed synthetic values. A mis-speci…ed imputation model can invalidate inferences based on the partially synthetic data, because the imputation model determines the distribution of s...

Statistical Disclosure Control: a genetic-based approach

Cell suppression is one of the most frequently used techniques to prevent the disclosure of sensitive data in statistical tables. Finding the minimum cost set of nonsensitive entries to suppress, along with the sensitive ones, in order to make a table safe for publication is a NP -hard problem denominated the Cell Suppression Problem (CSP ). Since statistical o¢ ces must publish safe tables rou...