The mathematical theory of differential privacy describes methods and practices that allow researchers to query sensitive datasets while controlling how much each query compromises the privacy of individuals contained in the dataset. This approach represents the cutting edge of privacy-protection, but one that is mathematically subtle and challenging to implement. Widespread use of these methods will require lowering the cost of adoption and adaptation. OpenDP is therefore producing a tested, trustworthy, interoperable, and flexible library of software that will make it easier for users to set up differentially private access to sensitive data. This grant provides continuing support for Harvard computer scientist Salil Vadhan, creator of OpenDP, as well as a dedicated community of theorists, engineers, practitioners, and privacy experts that is aiming to increase adoption of differential privacy. Now in its third year, OpenDP is shifting from a minimum viable product to a prospering ecosystem with heightened impact and broadened support. Specifically, grant funds allow Vadhan to expand OpenDP’s library capabilities to meet new application needs; promote OpenDP adoption among social science researchers; and further strengthen the growing community of experts using and contributing to OpenDP. Eventually, OpenDP will serve as a sustainable open-source library of tools and community dedicated to privacy-preserving data analysis.