Better differential privacy for end-to-end speech recognition - Amazon Science
Private aggregation of teacher ensembles (PATE) leads to word error rate reductions of more than 26% relative to standard differential-privacy techniques.| Amazon Science
Discover how Quantum AI protects your data—even against future-proof hackers. Learn how Quantum Federated Learning (QFL) combines speed, privacy, and resilience.| Blue Headline
Learn why a new study finds applying a trust graph to differential privacy could help companies improve security settings.| CDInsights
Private aggregation of teacher ensembles (PATE) leads to word error rate reductions of more than 26% relative to standard differential-privacy techniques.| Amazon Science
Calibrating noise addition to word density in the embedding space improves utility of privacy-protected text.| Amazon Science
Technique that mixes public and private training data can meet differential-privacy criteria while cutting error increase by 60%-70%.| Amazon Science
While I once hoped 2017 would be the year of privacy, 2024 closes on a troubling note, a likely decrease in privacy standards across the web. I was surprised by the recent Information Commissioner’s Office post, which criticized Google’s decision to introduce device fingerprinting for advertising purposes from| Security, Privacy & Tech Inquiries
Both secure multiparty computation and differential privacy protect the privacy of data used in computation, but each has advantages in different contexts.| Amazon Science
