High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA
By Ahmad Al Badawi, Bharadwaj Veeravalli, Chan Fook Mun, Khin Mi Mi Aung. 2018
96 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kim Laine
- M. Sadegh Riazi
- Blake Pelton
- Wei Dai
- Kimmo U. Järvinen
- Sujoy Sinha Roy
- Ingrid M. R. Verbauwhede
- Frederik Vercauteren
In Information Systems
- Wei Dai
- Kim Laine
- Blake Pelton
- M. Sadegh Riazi
- Furkan Turan
- Frederik Vercauteren
- Ingrid M. R. Verbauwhede
- Sujoy Sinha Roy
Citations in the corpus
- HEAX: An Architecture for Computing on Encrypted Data (2019, background): “Each M20K unit of BRAM holds 512-many 40-bit values.”
- FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data (2019, background): “Recent implementation in a high-end GPU [10] reduce the computation time by several factors.”
- Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme (2019, methodology): “Note that DSI_BFV only supports 30-bit moduli.”
- Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, methodology): “NTT has been ported to FPGAs [46], [50] and GPUs [2], [3], [17] to speedup polynomial multiplication.”