The objective is to propose a new method to generate synthetic data, which resemble the true data (utility) but preserve privacy. We propose to use vine copulas to model the true data, and then use truncation of the vine copula construction to protect against the identification of sensitive features of the true data. We work in the case when true data are tabular and the data are used for regression or classification.