Abstract
I will pitch and try to rally support for launching a community effort to build a system of tools for enabling privacy-protective analysis of sensitive personal data. Key among them will be an open-source library of algorithms for generating differentially private statistical releases, vetted and cumulated from leading researchers in differential privacy, and implemented for easy adoption by custodians of large-scale sensitive data. The hope is that this will become a standard body of trusted and open-source implementations of differentially private algorithms for statistical analysis and machine learning on sensitive data. It will magnify the impact of academic research on differential privacy, by providing a channel that brings algorithmic developments to a wide array of practitioners.