UTEP and PNNL create model to improve data security

EL PASO, TX – A group of researchers from El Paso and other regions of the United States have partnered to improve the privacy and security of sensitive data that may contain personally identifiable information.

Pacific Northwest National Laboratory (PNNL) data scientist Tony Chiang, DPhil, and University of Texas at El Paso (UTEP) Mathematical Sciences Professor Amy Wagler, Ph.D., are leading the project.

UTEP noted that the project uses machine learning models to transform sensitive real-world data into artificial data. According to the team, the artificial data will resemble the original data in all its statistical aspects and characteristics, but it can be shared without compromising the privacy of the contributors, and researchers will still be able to obtain valuable information from it.

Wagler said the work will be especially useful in the healthcare research sector, where providers may be concerned about sharing results and compromising patient confidentiality.

In addition, the computational model being developed by the team will analyze the original data and generate data that resembles it using statistical models. Then, another “adversarial” model will attempt to discriminate between the two data sets.

Wagler and Chiang have joint contracts with UTEP and PNNL. The appointments were established to help raise the productivity of researchers at both institutions by providing strategic capabilities that accelerate scientific impact.

A central component of the partnership between UTEP and PNNL is a commitment to investing in the future science, technology, engineering and mathematics (STEM) workforce by creating opportunities for students to have hands-on research experience, mentoring and experience in a national laboratory environment.