"Smartphone Privacy: From Fourier transforms to projective geometry and more"
featuring Jelani Nelson
Wednesday, March 9 at 7:00 pm ET (New York)
Many of us use smartphones and rely on tools like auto-complete and spelling auto-correct to make using these devices more pleasant, but building these tools creates a conundrum. On the one hand, machine-learning algorithms require data to "learn," but on the other hand, who among us is willing to send a carbon copy of all our text messages to phone manufacturers to provide that data? Join UC Berkeley computer scientist and engineer Jelani Nelson as he shares how mathematics can be used to construct schemes that, perhaps paradoxically, provably maintain user privacy while still allowing such machine-learning algorithms to learn from our data.
Special introduction by Christos Papadimitriou, Donovan Family Professor of Computer Science at Columbia University and author of Logicomix: An epic search for truth.
This event will take place in person at MoMath at 11 East 26th Street.
This page is for the 7:00 pm ET (New York) in-person session. Click here to register for a live-streamed session or for the 4:00 pm ET (New York) session instead.
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