Homomorphic Encryption Standard
By Martin R. Albrecht, Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Shai Halevi, Jeffrey Hoffstein, Kim Laine, Kristin E. Lauter and 6 more. 2019
295 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Wei Dai
- Shai Halevi
- M. Sadegh Riazi
- Blake Pelton
- Craig Gentry
- Ramesh Raskar
- Praneeth Vepakomma
In Computer Vision and Pattern Recognition
- Ramesh Raskar
- Serena Yeung
- Li Fei-Fei
- Shai Halevi
- Luc Van Gool
- Kristen Grauman
- Craig Gentry
- Trevor Darrell
Citations in the corpus
- A Review of Homomorphic Encryption Libraries for Secure Computation (2018, background): “Generation of such schemes continues to be an active area of research and development of standards for homomorphic encryption has recently taken-off as described in [20,21].”
- HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, methodology): “These parameters are selected based on the HE security standards [1] for 128-bit classical security.”
- Low Latency Privacy Preserving Inference (2018, methodology): “org/, with parameters that guarantee 128 bits of security according to the proposed standard for Homomorphic Encryptions (Albrecht et al., 2018).”
- Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference (2018, methodology): “This choice of coefficient modulus q meets the security standards established by the Homomorphic Encryption Standardization Workshop [6].”
- Compressible FHE with Applications to PIR (2019, methodology): “(Consulting Table 1 from [ACC + 18], using this cyclotomic ring with a modulus Q of size up to 111 bits yields security level of 128 bits.)”