*An additional $4,000 was awarded for posting their full code solution in an open source repository.
Team member Ryan McKenna, from UMass Amherst competed as a one-man team.
Team RMcKenna used the NIST Collaboration Space as their open source repository and can be accessed here. *Note that other contestant source code may also be found on this site.
At a high level, Team RMcKenna's algorithm is quite simple and can be broken up into two main steps.
More specifically, their algorithm combines three orthogonal ideas. These are listed below:
 Zhang, Jun, et al. "Privbayes: Private data release via bayesian networks." ACM Transactions on Database Systems (TODS) 42.4 (2017): 25.
 Chen, Rui, et al. "Differentially private high-dimensional data publication via sampling-based inference." Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.
 Abadi, Martin, et al. "Deep learning with differential privacy." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016.
 McKenna, Ryan, Miklau, Gerome, and Sheldon, Daniel. "Graphical-model based estimation and inference for differential privacy." Proceedings of the 36th International Conference on Machine Learning. 2019.