A Full RNS Variant of Approximate Homomorphic Encryption

By Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, Yongsoo Song. 2018

476 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

1. Kim Laine
2. Wei Dai
3. Hao Chen
4. M. Sadegh Riazi
5. Blake Pelton
6. Miran Kim
7. Yongsoo Song
8. Shai Halevi

In Information Systems

1. Wei Dai
2. Kim Laine
3. M. Sadegh Riazi
4. Blake Pelton
5. Hao Chen
6. Miran Kim
7. Yongsoo Song
8. Shai Halevi

Citations in the corpus

• HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “Similarly, we leveraged the RNS-level parallelism which is proven to be secure [16].”
• Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “For example, we implement full Residue Number System (RNS) variants of MKHE schemes and use an RNS-friendly decomposition method [4, 15, 30] for relinearization, thereby avoiding expensive high-precision arithmetic.”
• TFHE: Fast Fully Homomorphic Encryption Over the Torus (2019, result): “Optimized residue number system (RNS) implementation variants of BFV [9,45] and HEEAN [19] have recently been proposed and show a significant performance gain compared to their earlier respective implementation.”
• Secure Outsourced Matrix Computation and Application to Neural Networks (2018, unknown): “Another direction to explore is to employ a variant of HEAAN based on a Residue Number System (RNS) [13].”
• Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network (2021, methodology): “To reduce the complexity, the residue number system variant of the CKKS scheme [5], called the RNS-CKKS scheme, was also proposed.”
• HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “There exist full-RNS variants of HE schemes, such as those in [7, 37, 16], which do not require any RNS conversions except in decryption and show significant improvement over non-RNS variants.”
• OpenFHE: Open-Source Fully Homomorphic Encryption Library (2022, methodology): “FIXEDMANUAL: the RNS variant [8, 21] where modulus switching is manually done by the user.”