Protecting medical data for decision-making analyses

In this paper, we present a procedure for data protection, which can be applied before any model building based analyses are performed. In medical environments, abundant data exist, but because of the lack of knowledge, they are rarely analyzed, although they hide valuable and often life-saving knowledge. To be able to analyze the data, the analyst needs to have a full access to the relevant sources, but this may be in the direct contradiction with the demand that data remain secure, and more importantly in medical area, private. This is especially the case if the data analyst is outsourced and not directly affiliated with the data owner. We address this issue and propose a solution where the model-building process is still possible while data are better protected. We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data.