Deadline: 2019-05-06 Award: $150,000 Open to: Mathematicians, data scientists*
Are you a mathematician or data scientist interested in a new challenge? Then join this exciting data privacy competition with up to $150,000 in prizes, where participants will create new or improved differentially private synthetic data generation tools.
When a data set has important public value but contains sensitive personal information and can’t be directly shared with the public, privacy-preserving synthetic data tools solve the problem.
By mathematically proving that a synthetic data generator satisfies the rigorous Differential Privacy guarantee, we can be confident that the synthetic data it produces won’t contain any information that can be traced back to specific individuals in the original data.
The “Differential Privacy Synthetic Data Challenge” will entail 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.
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