DSI I-Cubed: Privacy Preserving Predictions with Antigoni Polychroniadou
Abstract: While big data technology offers great promise, it introduces a challenge for privacy. Can we achieve the best of both worlds, extracting the benefits that big data offers while providing privacy to the data owner? Essentially, we would like to perform complex computations while preserving privacy. In this talk I am going to talk about privacy preserving computation and show how cryptography, streaming data and machine learning can support new ways of organizing predictive analytics. Presenter: Antigoni Polychroniadou, J.P. Morgan AI research Presenter Bio: Antigoni Polychroniadou is a researcher at J.P. Morgan AI research and cryptographer lead in ROAR Data at J.P. Morgan. Antigoni was a junior Simons fellow, awarded by the Simons Society of Fellows, at Cornell Tech and a postdoctoral researcher in the Computer Science Department at Cornell University, hosted by Rafael Pass and Elaine Shi. Antigoni completed her Ph.D. at Aarhus University under the supervision of Ivan Damgård. She interned at IDC Herzliya, the Technion, University of California, Berkeley and IBM Research Thomas J. Watson. She holds an M.Sc. in mathematics of cryptography and communications from Royal Holloway University of London and B.Sc. in Computer Science and Economics from University of Macedonia, Greece.