Currently, an individual’s opportunities for privacy protection are limited by their digital skills and external factors such as illness, residence, and socio-economic status. Technological development in the field is happening very quickly, but coordination and common platforms are needed to increase accessibility. The purpose of the HEIDA project has been to develop software examples for homomorphic encryption based on RISE’s winning solution in the innovation competition Vinter. This will enable calculations on encrypted data and thus increase the individual’s opportunities for privacy protection in digital health. The project has also explored the usefulness of the technology with federated learning and analyzed the reliability, efficiency, and memory usage of calculations to provide insight into the practical application of the technology. This work also resulted in the development of a new type of neural network that is more resource-efficient during encryption. The technology is of interest to health care decision makers and IT professionals that consider solutions for end-to-end privacy protection in health data platforms.