Confidentiality - Data Swapping
NISS studied how to conduct data swapping, one of a number of techniques meant to distort confidentiality-threatening high-dimensional characteristics of a database while preserving non-threatening low-dimensional characteristics. Data swapping exchanges attribute values... more
Digital Government II: Data Confidentiality, Data Quality and Data Integration For Federal Databases: Foundations to Software Prototypes
Federal statistical agencies are the nation's largest gatherer and consumer of data. They are supposed to disseminate information and also protect the privacy of individuals and establishments that are described by the data. It is hard to find ways to get enough data to... more
Digital Government I
NISS was hired to help develop and build systems that expanded to Federal data but that preserved the confidentiality of the data and privacy of subjects. The systems would respond to queries from networked users of Federal data bases by performing and reporting statistical analyses that... more
Dynamics for Social Networks Processes: Comparing Statistical Models with Intelligent Agents
The goal of this project is to reconcile two methods for modeling change in social networks over time: a class of statistical models and intelligent agent models. The research contrasts the properties of these two approaches, exploring what qualitative dynamic behaviors in social networks are... more