BU’s Faculty To serve on Advisory Committee for The Federal Government

Study: Federal AI is a $1B market -- FCW

The federal data strategy is used by the Federal Government to create a 10-year vision on how to accelerate the use of data to support their mission, promote democracy and serve the public while using security measures to protect privacy and confidentiality.

The federal data strategy focuses on establishing an integrated data infrastructure with data practices to leverage the value of data as a strategic asset.

Mayank Varia, Co-Director of the Center for Reliable Information Systems & Cyber Security (RISCS), was recently nominated to serve on the Inaugural Federal Advisory Committee on data for evidence building by Senator Markey’s office. The Committee boasts a diverse background, with individuals from the government to experienced academics who focus on data for evidence building and utilizing sound data in federal policymaking. Privacy-enhancing technologies like “multi-party computation (MPC)” are a large part of that conversation, and Mayank is an international expert in MPC and related techniques for privacy. His research focuses on cryptography and its application to problems throughout and beyond computer science. He has developed cryptographically secure multi-party computation systems to measure Boston’s gender wage gap and identify repeat offenders of sexual assault.

Professor Varia was also recently chosen to lead the Law and Policy Section of the Task Team on Privacy-Preserving Techniques within the UN Global Working Group on Big Data. The team includes representatives from numerous governments and NGOs, in addition to academics.

Mayank Varia, a Research Associate Professor and Co-Director for the RISCS Center, leads the NSF Frontier MACS project. Prior to joining BU, he worked for four years at MIT Lincoln Laboratory.  At MIT Lincoln Lab, he designed and evaluated high-performance privacy-enhancing data search technology, created information-theoretic metrics to quantify privacy, and developed algorithms to capture linguistic provenance automatically.