Sens. Catherine Cortez Masto, D-Nev., and Deb Fischer, R-Neb., along with Reps. Haley Stevens, D-Mich., and Anthony Gonzalez, R-Ohio, are introducing bipartisan and bicameral legislation that would require the National Science Foundation (NSF) to support research on privacy enhancing technologies.

Along with consulting other relevant Federal agencies, NSF would support “merit-reviewed and competitively awarded research on privacy enhancing technologies.” Research may include:

  • Research on technologies for de-identification, pseudonymization, anonymization, or obfuscation of personal data in datasets while still ensuring fairness, accuracy, and efficiency;
  • Research on algorithms and other mathematical tools used to protect individual privacy when collecting, storing, sharing, or aggregating data;
  • Research on technologies to promote data minimization principles in data collection, sharing, and analytics; and
  • “Research awards on privacy enhancing technologies coordinated with other relevant Federal agencies and programs.”

“Digital data can be as personal and private as our social security number or information about our personal health,” said Senator Cortez Masto in a statement. “We must work to strike a healthy balance between privacy and innovation. This bipartisan, bicameral legislation helps us achieve that goal by researching ways in which privacy-enhancing technologies can complement emerging technologies of the 21st century.”

Additionally, the legislation would work to integrate this mission with NSF’s Computer and Network Security Program and would require the National Institute of Standards and Technology (NIST) to collaborate with academic, public, and private sectors in developing and establishing voluntary consensus standards for integrating privacy enhancing tech into business and governmental applications.

Read More About
Recent
More Topics
About
Jordan Smith
Jordan Smith
Jordan Smith is a MeriTalk Senior Technology Reporter covering the intersection of government and technology.
Tags