When data are stored in different locations and pooling of such data is not allowed, there is an informational loss when doing predictive modeling. In this thesis, a new method called Non-Disclosed Conformal Prediction (NDCP) is adapted into a regression setting, such that predictions and prediction intervals can be aggregated from different data sources without interchanging any data. The method is built upon the Conformal Prediction framework, which produces predictions with confidence measures on top of any machine learning method. The method is evaluated on regression benchmark data sets using Support Vector Regression, with different sizes and settings for the data sources, to simulate real life scenarios. The results show that the method produces conservatively valid prediction intervals even though in some settings, the individual data sources do not manage to create valid intervals. NDCP also creates more stable intervals than the individual data sources. Thanks to its straightforward implementation, data owners which cannot share data but would like to contribute to predictive modeling, would benefit from using this method.