Massive Data Institute

When a presidential election comes down to a handful of battleground states and a mere tens of thousands of votes, the accuracy of polls can significantly impact the outcome. "In many ways, polling shapes our reality at any given moment," said Michael Bailey, a professor at McCourt and the Georgetown University College of Arts & Sciences. In his new book, "Polling at a Crossroads," Bailey explains how polling is fundamentally broken and must be fixed if Americans want to live in a healthy, functional democracy. Read about it here: https://lnkd.in/eFfidZYQ

Like privacy enhancing technologies (PETs)? Here at the Massive Data Institute at the Georgetown University McCourt School of Public Policy, we’ve spearheaded a learning effort focused on PETs and how they can help folks conduct education research securely and in ways that protect sensitive information about schools and students. Last year, my colleague Stephanie Straus and I hosted a training series where we talked through the various PETs and discussed how they can be used to support different types of research that use educational data. If you’re curious about PETs, visit our website below and check out our 💡Trainings💡 and other resources on our website. Feel free to drop any questions in the comments below. 👇👇 https://lnkd.in/eDAzPb4F

We are excited to announce a call for applications for the Fall 2024 cohort of the MDI Scholars Program. For full consideration, the application deadline is June 21, 2024 at 11:59pm EST. Info and apply: https://lnkd.in/ei8_-Phf

When using education data in research, protecting student privacy is paramount. Thankfully, there are a ton of privacy enhancing technologies (PET) that can securely encrypt the data. But which is right for you? Secure Multiparty Computation (SMC), a very common PET, was the perfect solution for me and my colleague Amy O'Hara when we wanted to find out how much students receive on average in federal aid–broken out by income level, attendance patterns, and type of institution enrolled. Of course, we wanted to do this analysis without compromising any sensitive student information. Using SMC, we were able to effectively analyze National Center for Education Statistics records and National Student Loan Data System (NSLDS) data on federal aid amounts while the data (financial data and social security numbers) remained encrypted. Our study showed that SMC can be efficiently and effectively applied. What’s more, it can enable analyses that generate meaningful results that can inform policy decisions. The study also helped the Department of Education understand how SMC works and see the potential for using it to open up new opportunities for research collaboration across federal agencies. Learn more here: https://lnkd.in/gj6tscZ