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NORC clients are assured that we protect the privacy of their data while maximizing utility through implementation of the latest methodologies.

NORC takes data privacy and the protection of data very seriously. Our experts look for the best way to maintain data utility while preserving the privacy of each entity (person, business, school, hospital, etc.) with records in our datasets. This is especially crucial when data are shared with others outside of our systems.

The creation of shared data files, whether they are restricted use files (RUF) or open public use files (PUF), is a research area of wide interest. There are different ways to preserve privacy by anonymizing the data. The most common forms of anonymization techniques include Statistical Disclosure Limitation (SDL), data synthesis, and Differential Privacy (DP). NORC has conducted research in each of these areas and employs the techniques on projects across a wide range of clients. We can consult on the best way to protect a dataset and implement the necessary steps for anonymization. Furthermore, we analyze the resultant data to ensure that privacy is preserved and the data are safe to release and the data maintains analytic utility.

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Privacy Preserving Methodology Research Experts

Highlighted Projects

America’s DataHub Consortium

Demonstrating replicable processes for acquiring and providing secure access to linked data sources


National Center for Science and Engineering Statistics