Please note: This seminar will take place in DC 1304 and online.
Dr. Mina Dalirrooyfard, Vice President
Machine Learning Research, Morgan Stanley
With the increasing availability of publicly shared statistics derived from private datasets, safeguarding users’ personal information has become crucial. Differential privacy (DP) has emerged as a widely adopted framework for quantifying the level of individual privacy an algorithm preserves. Over the past decade, numerous fundamental algorithms have been studied within the context of DP.
This talk will focus on recent advances in the differentially private computation of graph cuts, including differentially private min-st-cut, Gomory-Hu tree and computing differentially private synthetic graphs that maintain all cuts. We will also touch on multiple interesting open problems in this area of research.
To attend this seminar in person, please go to DC 1304. You can also attend virtually on Zoom.