TFHE: Fast Fully Homomorphic Encryption Over the Torus
By Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, Malika Izabachène. Journal of Cryptology, 2019
1,179 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/927d10d78f107959df322e720fa1bb5210859e58
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
- Hao Chen
- Kristin E. Lauter
- Vinod Vaikuntanathan
- N. Xiong
- Pan Yang
- Jingli Ren
- Anantha P. Chandrakasan
In Computational Theory and Mathematics
- Kim Laine
- Hao Chen
- Kristin E. Lauter
- N. Xiong
- Pan Yang
- Jingli Ren
- Vinod Vaikuntanathan
- Anantha P. Chandrakasan
Citations in the corpus
- Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, background): “Recent efforts, both in theory and in practice have given us large gains in the performance of several types of PHE schemes and even FHE schemes [4], [7], [8], [15], [19], [32].”
- CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs (2018, methodology): “[9] use the TFHE library [17] for DNN inference.”
- Data Security and Privacy Protection for Cloud Storage: A Survey (2020, methodology): “In order to solve this problem, Moon and Lee [62] introduced TFHE [22] algorithm on the basis of the [20], and obtained higher performance comparison operation.”