CRC Team Research and Presentations

Directory of Related Research

CRC Team Research and Presentations

The Collaboration Research Cycle team strives to disseminate the project's resources and lessons learned to all relevant stakeholder communities. Below is a list of our presentations and publications to date. If there's a conference or workshop you think would be interested in learning more about the CRC, please let us know!

Below is a directory of individuals and groups doing related research using CRC resources. The directory is not comprehensive. Inclusion in the directory is not an endorsement from NIST. If you'd like your group to be added, follow the instructions here.

UMich Synthetic Data Evaluation

Jeremy Seeman (Michigan Institute for Data Science and Institute for Social Research)
Dhruv Kapur (College of Engineering)

We are researchers at the University of Michigan applying and extending NIST’s framework for evaluating synthetic data and its equity implications for ACS.

Keywords: Evaluation metrics, equity, meta-analysis of privacy algorithms


CRC Related Products: An Exploratory Meta-Analysis to Identify Outlying Behavior in the NIST Collaborative Research Cycle Archive

Point of contact: Jeremy Seeman (

Responsible AI for Science and Engineering (RAISE group)

Ferdinando Fioretto (Assistant Professor of Computer Science, UVA)
Saswat Das (Computer Science, UVA)
Razane Tajeddine (Department of Computer Science, University of Helsinki, Finland)
Pranav Putta (Computer Science, Georgia Tech)

We work on foundational topics relating to machine learning and optimization, privacy and fairness. We often ground our research in applications at the intersection of physical sciences and energy, as well as policy and decision making.


CRC Related Products: Examining Deidentified Data Quality using NIST Datasets and Tools


Matteo Giomi
Omar Ali Fdal
Nicola Vitacolonna

Privacy research team of Anonos, a data protection software vendor. Our research focuses on algorithms for data anonymization, such as synthetic data, and pseudonymization, as well as empirical privacy evaluations, attacks, differential privacy, machine learning and AI.

Keywords: Synthetic data, empirical privacy evaluations, privacy attacks


CRC Related Products: Anonymeter Application to CRC Diverse Communities Excerpts: A Privacy Perspective

Point of contact: Matteo Giomi (
Omar Ali Fdal (
Nicola Vitacolonna (

SynDiffix: Accurate Multi-table Synthetic Data

Paul Francis (Max Planck Institute for Software Systems, Open Diffix)

SynDiffix is an open-source Python package for generating highly accurate and strongly anonymous replicas of the original data. For low-dimensional queries, SynDiffix is easily an order of magnitude more accurate than other approaches. It is developed by the Max Planck Institute for Software Systems and Open Diffix.

Keywords: Accurate synthetic data, open-source, multi-table approach


CRC Related Products: A Comparison of SynDiffix Multi-table versus Single-table Synthetic Data

Point of contact: Paul Francis (