On the Utility of Synthetic Data

With the recent advances and increasing activities in data mining and analysis, the protection of the privacy of individuals is crucial. Several approaches address this concern, from techniques like data anonymisation to secure, non-disclosive computation, all of which have their specific strengths and weaknesses, depending on the specific requirements. A slightly different approach is the generation of synthetic data, which tries to preserve the overall properties and characteristics of the original data without revealing information about actual individual data samples. The promise is that, for most purposes, models trained on the synthetic data instead of the real data do not show a significant loss of performance. In this paper, we give an overview on currently available approaches for synthetic data generation, and empirically evaluate the utility of the generated synthetic data by testing them on a number of supervised machine learning tasks on several publicly available datasets.