The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

While rich medical, behavioral, and socio-demographic data are key to modern data-driven
research, their collection and use raise legitimate privacy concerns. Anonymizing datasets …

There is an ever increasing demand from researchers for access to useful microdata files.
However, there are also growing concerns regarding the privacy of the individuals contained …

The federal statistical system is experiencing competing pressures for change. On the one
hand, for confidentiality reasons, much socially valuable data currently held by federal …

In this paper, we propose the first formal privacy analysis of a data anonymization process
known as the synthetic data generation, a technique becoming popular in the statistics …

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 …

One goal of statistical privacy research is to construct a data release mechanism that
protects individual privacy while preserving information content. An example is a random …

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial
Census of Population and Housing has triggered renewed interest and debate over how to …

We study statistical risk minimization problems under a privacy model in which the data is
kept confidential even from the learner. In this local privacy framework, we establish sharp …

Administrative data are becoming increasingly important. They are typically the side effect of
some operational exercise and are often seen as having significant advantages over …