CS News:
U.Va. Leads $3M NSF Secure Computation Project
The National Science Foundation has awarded a $3 million grant to a team lead by Associate Professor David Evans for a project to develop privacy-preserving technologies.
Secure computation is a long-standing goal of computer science that aims to allow people to cooperate in computations without exposing their data. “The idea is that you can have two people compute a function that depends on things that each one knows individually and wants to keep private without exposing their private data to the other person, or to anyone else,” Evans says.
Among those working with Evans are computer science Assistant Professor abhi Shelat, public health sciences Professor Aaron Mackey in the School of Medicine, graduate student Yan Huang and colleagues at the University of Maryland and Indiana University.
Consider two smartphone users who would like to identify the common entries in their address books without revealing anything about their other contacts. “The two devices use cryptography to compute a function on encrypted data,” Evans says. “Both people learn the common entries, but can’t learn anything else about the address books because this information is encrypted for the entire computation.”
Another application for secure computation is the limited sharing of private medical information, such as an individual’s genome. A patient with a disease such as cancer might want to have his or her DNA compared with other patients who have the same disease and are undergoing treatments with various drugs. None of the patients in a database will want their information revealed, but shared information is essential to determining which drugs might work best in individual cases.
Unlike a normal computation, where each step operates on real data, in a secure computation each step uses encrypted data, and produces encrypted results. “The ultimate aim of this project is to make privacy-preserving computation practical and accessible enough to be used routinely,” Evans says.