3 citations · 3 across the 3 of their papers we have counts for
3 papers
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López +7
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated…
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…
ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies
Ş. S. Mağara, C. Yıldırım, F. Yaman +6
Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open f…