Machine learning (ML) models trained on personal data have been shown to leak
information about users. Differential privacy (DP) enables model training with a guaranteed …

Over the past decade, embeddings—numerical representations of machine learning
features used as input to deep learning models—have become a foundational data structure …

Companies increasingly expose machine learning (ML) models trained over sensitive user
data to untrusted domains, such as end-user devices and wide-access model stores. This …

Two recent shifts in computing are challenging the effectiveness of traditional approaches to
data protection. Emerging machine learning workloads have complex access patterns and …

Abstract Machine learning (ML) is transforming a wide range of applications, promising to
bring immense economic and social benefits. However, it also raises substantial security …