As a data-driven advertising company, Google's business model hinges on knowing as much about its users as possible. But as the public has increasingly awakened to its privacy rights, this imperative has generated more friction. One protection Google has invested in is the field of data science known as "differential privacy," which strategically adds random noise to user information stored in databases so that companies can still analyze it without being able to single people out. And now the company is releasing a tool to help other developers achieve that same level of differential privacy defense.
Today Google is announcing a new set of open source differential privacy libraries that not only offer the equations and models needed to set boundaries and constraints on identifying data, but also include an interface to make it easier for more developers to actually implement the protections. The idea is to make it possible for companies to mine and analyze their database information without invasive identity profiles or tracking. The measures can also help mitigate the fallout of a data breach, because user data is stored with other confounding noise.
"It's really all about data protection and about limiting the consequences of releasing data," says Bryant Gipson, an engineering manager at Google. "This way, companies can still get insights about data that are valuable and useful to everybody without doing something to harm those users."
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