Certain studies require sensitive datasets: the relationship between nutritious school lunch and student health, the effectiveness of salary equity initiatives, and so on. Valuable insights require navigating a minefield of private, personal information. Now, after years of work, cryptographers and data scientists at Google have come up with a technique to enable this "multi-party computation" without exposing information to anyone who didn't already have it.
Today Google will release an open source cryptographic tool known as Private Join and Compute. It facilitates the process of joining numeric columns from different datasets to calculate a sum, count, or average on data that is encrypted and unreadable during its entire mathematical journey. Only the results of the computation can be decrypted and viewed by all parties, meaning that you only get the results, not the data you didn't already own. The cryptographic principles the tool uses date back to the 1970s and '90s, but Google has repurposed and updated them to work with today's more powerful and flexible processors.
"The net result is that we can perform this computation without exposing any individual data and only getting the aggregate result," says Amanda Walker, director of privacy tools and infrastructure engineering at Google. "The naïve way to do this would be to take two sensitive data sets, dump them into a single database and do the join and the sum, but then you’ve got everything together and at risk of a data breach."
Lily Hay Newman covers information security, digital privacy, and hacking for WIRED.
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