Private aggregation of teacher ensembles (PATE) leads to word error rate reductions of more than 26% relative to standard differential-privacy techniques.| Amazon Science
Technique that mixes public and private training data can meet differential-privacy criteria while cutting error increase by 60%-70%.| Amazon Science
Both secure multiparty computation and differential privacy protect the privacy of data used in computation, but each has advantages in different contexts.| Amazon Science