Large genomic databases are indispensable for scientists looking for genetic variations associated with diseases. But they come with privacy risks for people who contribute their DNA. A 2013 study1showed that hackers could use publicly available information on the Internet to identify people from their anonymized genomic data.
To address those concerns, a system developed by Bonnie Berger and Sean Simmons, computer scientists at the Massachusetts Institute of Technology (MIT) in Cambridge, uses an approach called differential privacy. It masks the donor's identity by adding a small amount of noise, or random variation, to the results it returns on a user’s query. The researchers published their results in the latest issue of Cell Systems2. 
The system calculates the statistic that researchers want — such as the chance that one genetic variation is associated with a particular disease, or the top five genetic variations associated with an illness. Then it adds random variation to the result, essentially returning slightly incorrect information. For example, in a query for the top five genetic variations associated with a disease, the system might yield the top four genetic variations and the sixth or seventh variation.