An important question in the social sciences is how researchers can responsibly study large datasets containing confidential information about individuals, and how organizations can safely share such data while preserving the privacy of their users. Every query answered inevitably leaks some privacy. The only conceptual framework for specifying, measuring, and controlling such leakage is known as “differential privacy.” Imagine analysts who use a query mechanism to interrogate a dataset held by a trusted curator. An example of a differentially private mechanism would be one that returns an answer after adding a small amount of random noise drawn from a carefully selected distribution. That noise provably limits whether the analyst can even find out whether any given person is in the dataset, let alone anything else about that individual. Sloan has been an early funder of the development and application of this approach. This grant funds efforts by Salil Vadhan of the Harvard Privacy Tools Project to create a library of industrial-strength, open source differential privacy software called OpenDP. Like other open source development communities, OpenDP participants will cooperate to develop trusted, robust, and scalable tools that are easily accessible and adoptable in a wide variety of settings. Vadhan will convene a group of experts and users to guide the project’s overall architecture, features, progress, and sustainability planning.