The extensive collection and processing of personal information in big data analytics has
given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling …

Data is considered the new oil of the economy, but privacy concerns limit their use, leading
to a widespread sense that data analytics and privacy are contradictory. Yet such a view is …

This book helps practitioners gain a deeper understanding, at an applied level, of the issues
involved in improving data quality through editing, imputation, and record linkage. The first …

Data on individuals and entities are being collected widely. These data can contain
information that explicitly identifies the individual (eg, social security number). Data can also …

A reference to answer all your statistical confidentiality questions. This handbook provides
technical guidance on statistical disclosure control and on how to approach the problem of …

Sensitive personal data are created in many application domains, and there is now an
increasing demand to share, integrate, and link such data within and across organisations in …

Abstract k-Anonymity is a useful concept to solve the tension between data utility and
respondent privacy in individual data (microdata) protection. However, the generalization …

It is not uncommon in the data anonymization literature to oppose the “old” k k-anonymity
model to the “new” differential privacy model, which offers more robust privacy guarantees …

This paper provides an overview of methods of masking microdata so that the data can be
placed in public-use files. It divides the methods according to whether they have been …

The paper presents an illustration and empirical study of releasing multiply imputed, fully
synthetic public use microdata. Simulations based on data from the US Current Population …