Communications of the ACM
How to Share Personal Data While Keeping Secrets Safe
Credit: Maximilian Bode
Cornell University researchers have developed a new mathematical technique that allows for the sharing of large data sets of personal data without compromising any one individual's privacy.
"We want to make it possible for Facebook or the U.S. Census Bureau to analyze sensitive data without leaking information about individuals," says Colgate University professor Michael Hay, who helped develop the new technique while he was a researcher at Cornell.
The Cornell researchers used an approach called crowd-blending privacy, which involves limiting how a data set can be analyzed to ensure that any individual record is indistinguishable from a large group of other records. "The hope is that because crowd-blending is a less strict privacy standard it will be possible to write algorithms that will satisfy it, and it could open up new uses for data," Hay says.
Crowd-blending privacy has the potential to allow one to achieve much better utility by introducing less or no noise, according to University of Maryland professor Elaine Shi. "The underlying system architecture itself [would] enforce privacy—even when code supplied by the application developers may be untrusted," Shi says.
From Technology Review
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