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 …

Privacy-preserving data mining (PPDM) techniques aim to construct efficient data mining
algorithms while main-taining privacy. Statistical disclosure limitation (SDL) tech-niques aim …

Data sharing and access are venerable problems embedded in a rapidly changing milieu.
Pressure points include the increasingly data-driven nature of science, the volume …

Purpose Sharing of detailed individual-level data continues to pose challenges in multi-
center studies. This issue can be addressed in part by using analytic methods that require …

Considerable effort has gone into understanding issues of privacy protection of individual
information in single databases, and various solutions have been proposed depending on …

Over the past several years, the National Institute of Statistical Sciences (NISS) has
developed methodology to perform statistical analyses that, in effect, integrate data in …

Background Privacy-protecting analytic approaches without centralized pooling of individual-
level data, such as distributed regression, are particularly important for vulnerable …

Recent technological advances enable the collection of huge amounts of data. Commonly,
these data are generated, stored, and owned by multiple entities that are unwilling to cede …

This paper gives an overview of the two approaches to implement the Multivariate Logistic
Regression. There is the Geometric way and the Probabilistic way. The aim of this paper is …

Integrating multiple databases that are distributed among different data owners can be
beneficial in numerous contexts of statistical analysis. Unfortunately, the actual sharing of …