45 citations · 72 across the 5 of their papers we have counts for
6 papers
FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption
Lohit Daksha, Seyda Guzelhan, Kaustubh Shivdikar +10
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by seve…
FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs
Carlos Agulló-Domingo, Óscar Vera-López, Seyda Guzelhan +7
Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving appr…
NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator
Kaustubh Shivdikar, Nicolas Bohm Agostini, Malith Jayaweera +5
Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Des…
GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption
Kaustubh Shivdikar, Yuhui Bao, Rashmi Agrawal +9
Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure…
Accelerating Polynomial Multiplication for Homomorphic Encryption on GPUs
Kaustubh Shivdikar, Gilbert Jonatan, Evelio Mora +6
Homomorphic Encryption (HE) enables users to securely outsource both the storage and computation of sensitive data to untrusted servers. Not only does HE offer an attractive soluti…
Does Fully Homomorphic Encryption Need Compute Acceleration?
Leo de Castro, Rashmi Agrawal, Rabia Yazicigil +4
Fully Homomorphic Encryption (FHE) allows arbitrarily complex computations on encrypted data without ever needing to decrypt it, thus enabling us to maintain data privacy on third-…