OpenMined

OpenMined is a 501(c)(3) non-profit foundation, founded in 2017, that develops open-source privacy-enhancing network technology. Its stated purpose is to allow insights to be drawn from non-public data without copying that data or granting direct access to it. The organisation operates as a global, mission-driven community of more than 18,000 members, bringing together practitioners in AI, security and privacy, and is volunteer- and contributor-led rather than conventional in structure.

The technical work spans privacy-preserving machine learning, federated learning, differential privacy, secure multi-party computation, homomorphic encryption, AI security and attribution-based control for AI, alongside open-source network protocol development. Three main outputs anchor this effort: SyftBox, a protocol for building privacy-preserving applications; PySyft, an open-source library for secure machine learning; and Attribution-Based Control, a paradigm for safe, trustworthy AI. OpenMined's premise is that AI currently trains and evaluates on less than 0.01% of the world's data, and that data sovereignty - data staying where it lives rather than being surrendered to centralised providers - is the constraint worth addressing.

Work is applied across healthcare, hospitals, climate science, AI safety, research and academia, and technology. The emphasis throughout is on protecting privacy, security, legal and intellectual property interests while still enabling research, with the stated goal that the right questions be answered by the right people for the right reasons. The foundation's presence is global and its output is open-source.