PRIVASA - Privacy Preserving AI for Synthetic and Anonymous Health Data
PRIVASA -Privacy Preserving AI for Synthetic and Anonymous Health Data
Secure data exchange and storage are key features in any medical imaging system. Strict privacy preserving strategies can be a major obstacle for improving patient care through AI development. The apparent conflict between patient privacy and RDI goals is resolved by designing next-generation data analysis tools.
PRIVASA designs and develops a data analysis framework, that allows enterprises to develop their software products on encrypted data from multiple institutions, hospitals, and clinics without sharing the patient data. To achieve this, the project applies federated, secure and privacy-preserving artificial intelligence (AI). PRIVASA focuses on medical imaging applications, considering potential clinical benefits and prospects in medical imaging and beyond.
PRIVASA consortium, together with academic and industrial partners, propose a data-driven research, development, and innovation approach to facilitate collection and sharing of medical image data that meets strict data protection criteria. Project aim to bring agility and flexibility to accelerate product development of AI enterprises operating in Finland and international healthcare businesses and markets. Ultimately, in PRIVASA the main motivation is to create a viable privacy protecting ecosystem leveraging a competitive advantage based upon unique knowledge and open source tools, enabling participating commercial actors and companies to compete in the scalable health technology sector –foremostly in Finland, but potentially also in EU region, United States, Asia, and developing countries.