The mathematical theory of differential privacy describes methods and practices that can be implemented that allow researchers to query datasets with sensitive information while monitoring how much each query threatens the privacy of the individuals contained in the dataset. Differentially private methods are the current cutting edge of privacy-protecting science, yet, they are often mathematically complex and difficult to implement for those not versed in them. Widespread use of these methods will require mediating institutions that lower the cost of adoption, trusted places where researchers can download easy-to-install and easy-to-use software applications that will allow them to apply differentially private firewalls to sensitive data. In response to this need, Harvard computer scientist Salil Vadhan has created OpenDP, a dedicated community of theorists, engineers, practitioners, and privacy experts that are aiming to increase adoption of differential privacy by producing an open source suite of flexible, tested, and industrial-strength software components that makes implementing differential privacy both straightforward and trustworthy. Funds from this grant will support the effort, allowing Vadhan to further develop the library of general-purpose differential privacy algorithms, attract new experts to the collaboration, form new partnerships with corporations interested in protecting sensitive data, promoting awareness of the collaboration and its tools, and holding an annual meeting of stakeholders and users from academia, government and industry.