Lunch and Learn with Ferdinando Fioretto
11:00am - 12:00pm
Engineering & Computer Science, Room 410, Classroom
Advances in artificial intelligence and data science have allowed the development of products that leverage individuals' data to provide valuable services. However, the use of this massive quantity of personal information raises fundamental privacy concerns. Differential Privacy (DP) has emerged as the de-facto standard to addresses the sensitivity of such information and can be used to release privacy-preserving datasets. In this talk, Fioretto will focus on the problem of releasing privacy-preserving data for complex data analysis tasks. He will introduce the notion of Constrained-Based Differential Privacy (CBDP) which allow us to cast the data release problem to an optimization problem whose goal is to preserve the salient features of the original dataset. Finally, he will discuss two applications of CBDP for large socio-technical systems.