Privacy preservation is important for machine learning and datamining, but measures designed to protect private information sometimes result in a trade off: reduced utility of the training samples. This thesis introduces a privacy preserving approach that can be applied to decision-tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of privacy of collected data samples in cases when information of the sample database has been partially lost. This approach converts the original sample datasets into a group of unreal datasets, where an original sample cannot be reconstructed without the entire group of unreal datasets. This approach does not perform well for sample datasets with low frequency, or when there is low variance in the distribution of all samples. However, this problem can be solved through a modified implementation of the approach introduced later in this thesis, by using some extra storage.