We are proud to announce the winners in the Unlinkable Data Challenge: Advancing Methods in Differential Privacy!


GRAND PRIZE: $15,000

Georgia Tech Privacy Team: Differentially Private Generative Adversarial Network (DP-GAN)  to generate private synthetic data for analysis tasks.

Check out the details here.


RUNNER-UP: $10,000

DPSyn: Generate a synthetic dataset that approximates many randomly-chosen marginal distributions of the input dataset.

Check out the details here.



WesTeam: Real solutions from the statistical community for differentially private and high-quality data releases by national statistical institutes.

Check out the details here.



  1. Georgia Tech Privacy Team
  2. DPSyn


Want more? Join us for Phase Two with the NIST Differential Privacy Synthetic Data Challenge hosted on TopCoder!

The second phase of this contest will move from ideas to implementation, focusing on empirical evaluation of differentially private synthetic data generation tools.  In this head-to-head Topcoder Marathon Match, teams will implement their solution and be benchmarked against each other on clustering, classification and regression tasks. You are eligible to participate in Phase 2 whether or not you participated in Phase 1.

Be on the lookout for more details when the challenge launches this fall!