Differential privacy technology has passed a preliminary transition from being the subject of academic work to initial implementations by large organizations and high-tech companies that have the expertise to develop and implement customized differentially private methods. With a growing collection of software packages for generating differentially private releases from summary statistics to machine learning models, differential privacy is now transitioning to being usable more widely and by smaller organizations. This webinar explains how administrative data containing personal information can be collected, analyzed, and published in a way that ensures the individuals in the data will be afforded the strong protections of differential privacy.
- Presenters: Alexandra Wood (Harvard University), Micah Altman (Massachusetts Institute of Technology), Kobbi Nissim (Georgetown University), Salil Vadhan (Harvard University)
- Date: February 1, 2021, at 11am EST
- Join the webinar