Sustainable, secure and fast medical data

How to process, store and distribute high sensitive medical data efficiently? How to ensure privacy? What novel methods and algorithms are needed? How to speed up the process of developing, validating and using medical data by engineering methods?

Storing Data Securely and processing Big Medical Databases efficiently is a basic requirement for any health technology of the 21st century. KASTEL is KITs forefront security institute,  High Data throughput using computational methods are the specialty of the Mathsee KIT-Center, while joint big data driven research and innovation efforts are performed by labs as SDIL and SDSC-BW. Because security and performance is so critical, we attack the problem from various levels: From the data level by using encryption technologies, from the personal level using technologies such as special DLT-based methods or AI explanation methods for higher trust in the system and its protection, from the tools level by using automated verification technologies to ensure correctness of data and system.


Approaching Privacy vs. Utility using data driven approaches (Böhm, (Strufe, Stiefelhagen))

Speeding Up Processing and Validation of Medical Big Data (Beigl)

Operating Medical Big Data (Frank)

DLT based methods and Privacy-preserving Dataspaces (Sunyaev)

Managing risks in digitalized medical service (Schultmann, Kaiser)


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