  Synthetic data has been widely applied in the real world recently. One
typical example is the creation of synthetic data for privacy concerned
datasets. In this scenario, synthetic data substitute the real data which
contains the privacy information, and is used to public testing for machine
learning models. Another typical example is the unbalance data over-sampling
which the synthetic data is generated in the region of minority samples to
balance the positive and negative ratio when training the machine learning
models. In this study, we concentrate on the first example, and introduce (a)
the Howso engine, and (b) our proposed random projection based synthetic data
generation framework. We evaluate these two algorithms on the aspects of
privacy preservation and accuracy, and compare them to the two state-of-the-art
synthetic data generation algorithms DataSynthesizer and Synthetic Data Vault.
We show that the synthetic data generated by Howso engine has good privacy and
accuracy, which results the best overall score. On the other hand, our proposed
random projection based framework can generate synthetic data with highest
accuracy score, and has the fastest scalability.
