Bootstrapping for Approximate Homomorphic Encryption

By Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, Yongsoo Song. IACR Cryptology ePrint Archive, 2018

436 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/0bc0416a83d3ff94dc703b6c1d110f50e4231c05

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. Miran Kim
5. Yongsoo Song
6. Shai Halevi
7. Kristin E. Lauter
8. Vinod Vaikuntanathan

In Computational Theory and Mathematics

1. Kim Laine
2. Miran Kim
3. Hao Chen
4. Shai Halevi
5. Yongsoo Song
6. Wei Dai
7. Kristin E. Lauter
8. Vinod Vaikuntanathan

In Information Systems

1. Wei Dai
2. Kim Laine
3. Hao Chen
4. Miran Kim
5. Yongsoo Song
6. Vinod Vaikuntanathan
7. Shai Halevi
8. Andrey Kim

Citations in the corpus

• CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs (2018, methodology): “[15] describes a bootstrapping algorithm for the HEAAN [16] scheme, and best results takes about 1 second to bootstrap a single number.”
• Secure Outsourced Matrix Computation and Application to Neural Networks (2018, neutral): “For further details, we refer to [14] and [12].”
• F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (2021, methodology): “For CKKS, we use non-packed CKKS bootstrapping from HEAAN [16], also with Lmax = 24.”
• Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “Furthermore, we adapt the state-of-the-art bootstrapping algorithms for these schemes [9, 12, 14] to the multi-key scenario to build Multi-Key Fully Homomorphic Encryptions with packed ciphertexts.”
• Logistic regression model training based on the approximate homomorphic encryption (2018, methodology): “In terms of asymptotic complexity, applying the bootstrapping method of approximate HE scheme [30] to the GD algorithm would achieve a linear computation cost on the iteration number.”
• Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network (2021, methodology): “In addition, we implement bootstrapping of the RNS-CKKS scheme in the SEAL library according to [6]–[10] to support a large number of homomorphic operations for a deep neural network, as the SEAL library does not support the bootstrapping operation.”
• OpenFHE: Open-Source Fully Homomorphic Encryption Library (2022, positive): “• Approximate bootstrapping is implemented for the CKKS scheme using the RNS design described in [7, 8, 14, 19, 22].”