All Times EDT
Keywords: differential privacy, data privacy, open-source
Since it was introduced in 2006, differential privacy has become accepted as a gold standard for ensuring that individual-level information is not leaked through statistical analyses or machine learning on sensitive datasets. In recent years, it has seen large-scale deployments by Google, Apple, and the US Census Bureau, all organizations with the resources and expertise to implement their own custom systems for differential privacy. In this talk, I will describe our efforts to foster wider adoption of differential privacy, focusing on OpenDP, a new community effort to build a suite of trusted, open-source tools for enabling differentially private analysis of sensitive personal data. This is joint work with many collaborators in the Privacy Tools Project (http://privacytools.seas.harvard.edu/).