In the two scenarios of conventional data analysis setting, organizations either analyze their data in isolation (a) or share their data with a third party or cloud to enable data integration (b).
Data sharing is naturally linked to many challenges, such as data privacy concerns, data transferring costs and data heterogeneity.
Hence, in many cases, organizations prefer to analyze their data in isolation, losing many benefits of data collaboration.

Our collaborative research project aims to enable the transition to Society 5.0 through the new technological advancements in Big Data analysis.
Our short-term goal is to develop the fundamental technology for data collaboration on the medical and health data and to demonstrate the applications of our method in partnership with hospitals and medical providers.

We set the following immediate goals for the short term:

• To develop a basic algorithm for the variation-preserving dimensionality reduction
•  To develop specialized Machine Learning techniques suitable for intermediate representations of multi-modal data
• To demonstrate the work of the developed technology on distributed datasets of medical data
• To test the privacy guarantees of the developed technology

Long-term goals:

• To create a deployable toolkit/software with the developed technology
• To test the method on various applications