Abstract: This is a whole-lifecycle encrypted big data analysis method and system for the data from the different sources. The method performs unified local modeling of data from multiple data sources and transmitting them to a secure computing server after encryption. This secure computing server then processes and analyzes the data, including feature extraction, model training and model validation. The system includes multiple data sources corresponding to the described method and a secure computing server, for providing data, analyzing and processing the data. By processing and encrypting the data locally, and by supporting secure data sharing and federated learning of multiple data sources, this invention achieves protection of the data sources and addresses the privacy and security issues of the raw data for cross-institution big data collaborative analysis.

Abstract: The system disclosed implements a secure method for facilitating secure exchange of health information among various stakeholders, including data owners or contributors, data requestors or miners, and medical providers, including hospitals, clinics, and research laboratories. Additional aspects of the system provide means for conducting secure research on health data collected from data contributors. Health information is exchanged using a decentralized system that incentivizes data contributors to provide health data to data miners. The data miners, which may be pharmaceutical companies, medical laboratories, or hospitals, use various methods in order to perform research on aggregated contributor data, while maintaining contributor privacy.