Video Abstract: Private AI


June 30, 2017


Rich Caruana, Jung Hee Cheon, Kristin Lauter


Microsoft, Seoul National University, Microsoft


As the volume of data goes up, the quality of machine learning models, predictions, and services will improve. Once models are trained, predictive cloud services can be built on them, but users who want to take advantage of the services have serious privacy concerns about exposing consumer and enterprise data—such as private health or financial data—with machine learning services running in the cloud. Recent developments in cryptography provide tools to build and enable “Private AI,” including private predictive services that do not expose user data to the model owner, and that also provide the means to train powerful models across several private datasets that can be shared only in encrypted form. This session will examine the state of the art for these tools, and discuss important directions for the future of Private AI.