Biomedical research is facing tremendous growth of data due to the rapid progression with analytics technologies. Researchers deal with large data sets of genomics, proteomics, and metabolomics data, together with imaging exceeding gigapixels per image and large-scale text mining from the clinical unstructured text records in different languages and information coding systems. Particular for medical research is dealing with resources coming from individual persons, whose privacy needs to be protected and who give their consent for sharing material and data for research purposes. Human-oriented biobanks have become infrastructural hubs for collecting biological samples and the data, analyzing the samples to generate additional data, harmonizing the data and sharing it with biomedical researchers. The talk will address the challenges and possible solutions for storing and processing privacy-sensitive data in a scalable way, which has turned into major collaboration between biomedical researchers and computer scientists as well as IT infrastructures.