2 papers
cs.CR2026
CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption
Cory Brynds, Parker McLeod, Lauren Caccamise +5
Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promisin…
cs.CR2025
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs
Aidan Ferguson, Perry Gibson, Lara D'Agata +5
The deployment of deep neural networks (DNNs) in privacy-sensitive environments is constrained by computational overheads in fully homomorphic encryption (FHE). This paper explores…