Accelerating leveled fully homomorphic encryption using GPU
By Wei Wang, Zhilu Chen, Xinming Huang. 2014
45 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
- Michael Naehrig
- Thomas Pöppelmann
- M. Sadegh Riazi
- Blake Pelton
- Wei Dai
- Nicholas Genise
- Srini Devadas
In Information Systems
- Kim Laine
- Michael Naehrig
- Wei Dai
- M. Sadegh Riazi
- Blake Pelton
- Daniel Sánchez
- Adrián Macías
- Srini Devadas
Citations in the corpus
- HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “In [58], a GPU-based implementation of BGV scheme [11] is introduced.”
- HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, methodology): “In [71], a GPU-based implementation of BGV scheme [11] is introduced.”
- Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, background): “These may be reasons that previous work mainly focuses on using GPUs [19,40,41] and ASICs [21,44], and that FPGA implementations either work only with small parameters and on-chip memory [11] or explicitly do not take into account the complexity of transferring data between an FPGA and a host [9,35]”
- F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (2021, methodology): “GPU acceleration: Finally, prior work has also used GPUs to accelerate different FHE schemes, including GH [74, 75], BGV [73], and B/FV [1].”