Learning and inference from sensitive data
VirtualSpeaker: Adam Smith (Boston University) Title: Learning and inference from sensitive data Abstract: Consider an agency holding a large database of sensitive personal information—say, medical records, census survey answers, web searches, or genetic data. The agency would like to discover and publicly release global characteristics of the data while protecting the privacy of individuals’ records. I will discuss recent (and not-so-recent) results on this problem with […]