  Data is the lifeblood of the modern world, forming a fundamental part of AI,
decision-making, and research advances. With increase in interest in data,
governments have taken important steps towards a regulated data world,
drastically impacting data sharing and data usability and resulting in massive
amounts of data confined within the walls of organizations. While synthetic
data generation (SDG) is an appealing solution to break down these walls and
enable data sharing, the main drawback of existing solutions is the assumption
of a trusted aggregator for generative model training. Given that many data
holders may not want to, or be legally allowed to, entrust a central entity
with their raw data, we propose a framework for the collaborative and private
generation of synthetic tabular data from distributed data holders. Our
solution is general, applicable to any marginal-based SDG, and provides input
privacy by replacing the trusted aggregator with secure multi-party computation
(MPC) protocols and output privacy via differential privacy (DP). We
demonstrate the applicability and scalability of our approach for the
state-of-the-art select-measure-generate SDG algorithms MWEM+PGM and AIM.
