In the last few decades the data were mainly a by-product of the daily operations. In general, not much was used for analytical purposes other than reporting and simple statistics. Just recently it became clear that data are important assets if used for analyses that help decision-making. To be able to analyze the data, one needs to have a full access to the relevant sources. This may contradict on of the paramount requirements to have secure, private data, especially if the data analyst is outsourced and not directly affiliated with the data owner, as is many times the case in medical environments. In this paper we present data analyses from the data protection point of view. We propose a solution for outsourced model-based data analyses. A formal framework for protecting the data that leave the organization?s boundary, based on relational data model abstract data type is presented. The data and the data structure are modified so that the process of data analysis can still take place and the results can be obtained, but the data content itself is hard to reveal. Once the data analyses results are returned, the inverse process discloses the meaning of the model to the data owners.