First, we subsample the dataset to reduce any individual’s impact on the data smaller to reduce the amount of noise we need to add for differential privacy. Then, we use information from the training data to smooth out the noisy counts generated by applying the standard Laplace mechanism to this reduced data.
Place: N/A
Prize amount: $1,000 (Progressive Prize)
Team members: Zachary Schutzman