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Practical Data Synthesis for Large Samples

67-97

On Regression-Tree-Based Synthetic Data Methods for Business Data

A New Data Collection Technique for Preserving Privacy

99-129

Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples

Multiple Imputation for Disclosure Limitation: Future Research Challenges

Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data

Statistical Disclosure Limitation: New Directions and Challenges

Model Selection when multiple imputation is used to protect confidentiality in public use data

A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms

Estimation of Regression Parameters from Noise Multiplied Data

Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets

Releasing Microdata: Disclosure Risk Estimation, Data Masking and Assessing Utility

Towards a Systematic Analysis of Privacy Definitions

How Will Statistical Agencies Operate When All Data Are Private?

Privacy Protection from Sampling and Perturbation in Survey Microdata

Partial Information Releases for Confidential Contingency Table Entries: Present and Future Research Efforts

An Axiomatic View of Statistical Privacy and Utility

Synthetic Business Microdata

an Australian example