Sensitive data, especially in the era of GPDR, is under strict regulations and access restrictions. This is a major obstacle for conducting research with the data, as analysts first have to be granted access, requiring a lengthy approval process and often a purpose for accessing the data. Following, they are limited to secure computing resources, which limits their flexibility in choosing the algorithms and tools for their analysis. Data synthesis promises to be a solution to the sharing of sensitive datasets, by acting as a layer of anonymization which aids in reducing access restrictions. Methods for privacy-aware data synthesis of large data under secure with the constraint of limited computing resources is a promising research area, with the goal of liberating sensitive data for research purposes.