When Apple discovers trending popular emojis, or when Google reports traffic at a busy restaurant, they’re analyzing large datasets made up of individual people. Those people’s personal information is systematically protected thanks in large part to research by Harvard computer scientists. Now, after two decades of work on the cryptography-adjacent mathematical framework known as differential privacy, researchers in the John A. Paulson School of Engineering and Applied Sciences have reached a key milestone in moving privacy best practices from academia into real-world applications.
Who is using differential privacy? A new registry aims to make it visible
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