|WHITE_PAPER 2020.pdf||577 KB|
- View a talk on this paper presented at the 2020 OpenDP Community Meeting
- View a talk on this paper presented at TPDP 2020
OpenDP is a community effort to build a trustworthy suite of open-source tools for enabling privacy-protective analysis of sensitive personal data, focused on a library of algorithms for generating differentially private statistical releases. The target use cases for OpenDP are to enable government, industry, and academic institutions to safely and confidently share sensitive data to support scientifically oriented research and exploration in the public interest. We aim for OpenDP to flexibly grow with the rapidly advancing science of differential privacy, and be a pathway to bring the newest algorithmic developments to a wide array of practitioners.
OpenDP is led by Faculty Directors Gary King and Salil Vadhan and an Executive Committee at Harvard University, funded in part by a grant from the Sloan Foundation. Its efforts so far have included implementing a differentially private curator application in collaboration with Microsoft, and developing a framework for a community-driven OpenDP Commons through the work of an Ad Hoc Design Committee including external experts. Going forward, the project plans to engage with a wide community of stakeholders, establish partnerships with a wide variety of groups from industry, academia, and government, and adopt increasing levels of community governance.