Abstract
Anand Sarwate: Challenges in Privacy-Preserving Learning for Collaborative Research Consortia
Protecting privacy is particularly important in applications involving human health data due to ethical, legal, and institutional regulations. However, in order to learn from larger populations, research institutions need to collaborate by performing joint analyses on locally-held data. While many statistical analyses can be performed such that data holders need only share data derivatives, differentially privacy can give quantifiable privacy protections at the expense of loss in utility/accuracy. Privacy protections can incentivize more institutions to share access to their data. At the same time, typical sample sizes in some applications may be too small to support strong privacy protections, and certain tasks may be more amenable to differential privacy than others. This talk will discuss some of these issues and the corresponding theoretical challenges in the context of designing a collaborative research system for neuroimaging data.