NIST Differential Privacy Synthetic Data Challenge

Congratulations to the Top 5 winners, and to all contestants in this Challenge!  The winners in Match #3 of the Differential Privacy Synthetic Data Challenge are:

1-st ($25,000) - 902,307 - rmckenna
2-nd ($15,000) - 870,097 - ninghui
3-rd ($10,000) - 823,512 - privbayes
4-th ($5,000) - 768,802 - gardn999
5-th ($3,000) - 541,494 - manisrivastava

And the progressive prize winners ($1,000 each), already announced earlier, are:

rmckenna
ninghui
privbayes
gardn999

For more details on the final review and scoring, head over to the Topcoder forum. 

Congratulations to all winners! You are helping NIST grow a diverse community that is maturing into robust solutions.  Your participation in this data challenge is incredibly important…highlighting different approaches that will become the basis for future growth and innovation while helping NIST establish a measurement-based approach to fostering data-driven R&D in this area.  Your involvement with PSCR ensures that this community considers the practical, applied use of differential privacy in public safety applications of the future.

Did you attend the Match #2 Webinar? 

We answered your most pressing questions. If you missed it, you can watch the recording below and review the written Q&A here.

As a reminder, we are quickly approaching the Match 2 deadline on February 9, 2019 at 21:00 EST (New York). 

If you have any additional questions, please head over to the Topcoder Forums and we will do our best to answer them for you.

We are hosting a Q & A webinar with the NIST and Topcoder on Tuesday, Jan 15 at 12:30 pm ET (New York). This will be a great opportunity to meet the sponsors, learn more, and ask your burning questions. 

Save Your Seat

We are thrilled to announce that the second marathon match officially opens for submission today!

The Differential Privacy Synthetic Data Challenge entails a sequence of three marathon matches run on the Topcoder platform, asking contestants to design and implement their own synthetic data generation algorithms, mathematically prove their algorithm satisfies differential privacy, and then enter it to compete against others’ algorithms on empirical accuracy over real data, with the prospect of advancing research in the field of Differential Privacy.

Anyone is welcome to participate in Match 2, regardless of whether or not you participated in Match 1. If you’re not a differential privacy expert, and you’d like to learn, we’ll have tutorials to help you catch up and compete!

For more information on this data challenge funded through Public Safety Communications Research (PSCR) at NIST, or any federal government challenge, go to Challenge.gov.

We are also hosting a webinar on Tuesday, Jan 15 at 12:30 pm ET (New York). This will be a great opportunity to meet the sponsors, learn more, and ask any questions. You can register for the webinar here.

Head over to the Topcoder Challenge Page to view the full details

Congratulations to the Top 5 winners, and to all contestants in this Challenge!  The winners in Match #1 of the Differential Privacy Synthetic Data Challenge are:

1st ($10 000) - 781 953 - jonathanps
2nd ($7 000) - 736 780 - ninghui
3rd ($5 000) - 664 623 - rmckenna
4th ($2 000) - 93 955 - manisrivastava
5th ($1 000) - 82 414 - privbayes

For more details on the final review and scoring, head over to the Topcoder forum.

Differential privacy is an emerging research area. The work that each of you have done and will continue to do is critical to developing the knowledge and resources essential to expanding research in this area. You are helping NIST grow a diverse community that is maturing into robust solutions.  Your participation in this data challenge is incredibly important…highlighting different approaches that will become the basis for future growth and innovation while helping NIST establish a measurement-based approach to fostering data-driven R&D in this area.  Your involvement with PSCR ensures that this community considers the practical, applied use of differential privacy in public safety applications of the future.