| Privacy-preserving machine learning with fully homomorphic encryption for deep neural network JW Lee, HC Kang, Y Lee, W Choi, J Eom, M Deryabin, E Lee, J Lee, ... iEEE Access 10, 30039-30054, 2022 | 337 | 2022 |
| Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions E Lee, JW Lee, J Lee, YS Kim, Y Kim, JS No, W Choi International Conference on Machine Learning, 12403-12422, 2022 | 129 | 2022 |
| High-precision bootstrapping of RNS-CKKS homomorphic encryption using optimal minimax polynomial approximation and inverse sine function JW Lee, E Lee, Y Lee, YS Kim, JS No Advances in Cryptology–EUROCRYPT 2021: 40th Annual International Conference …, 2021 | 110 | 2021 |
| Minimax approximation of sign function by composite polynomial for homomorphic comparison E Lee, JW Lee, JS No, YS Kim IEEE Transactions on Dependable and Secure Computing 19 (6), 3711-3727, 2021 | 79 | 2021 |
| Precise approximation of convolutional neural networks for homomorphically encrypted data J Lee, E Lee, JW Lee, Y Kim, YS Kim, JS No IEEE Access 11, 62062-62076, 2023 | 72 | 2023 |
| Optimization of homomorphic comparison algorithm on rns-ckks scheme E Lee, JW Lee, YS Kim, JS No IEEE Access 10, 26163-26176, 2022 | 35 | 2022 |
| Modification of frodokem using gray and error-correcting codes E Lee, YS Kim, JS No, M Song, DJ Shin IEEE Access 7, 179564-179574, 2019 | 16 | 2019 |
| Rotation key reduction for client-server systems of deep neural network on fully homomorphic encryption JW Lee, E Lee, YS Kim, JS No International Conference on the Theory and Application of Cryptology and …, 2023 | 10 | 2023 |
| Optimal Minimax Polynomial Approximation of Modular Reduction for Bootstrapping of Approximate Homomorphic Encryption. JW Lee, E Lee, Y Lee, YS Kim, JS No IACR Cryptol. ePrint Arch. 2020, 552, 2020 | 8 | 2020 |
| Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network. IEEE Access, Vol. 10 (2022), 30039--30054 JW Lee, HC Kang, Y Lee, W Choi, J Eom, M Deryabin, E Lee, J Lee, ... | 7 | 2022 |
| Optimizing layerwise polynomial approximation for efficient private inference on fully homomorphic encryption: a dynamic programming approach J Lee, E Lee, YS Kim, Y Lee, JW Lee, Y Kim, JS No arXiv preprint arXiv:2310.10349, 2023 | 6 | 2023 |
| Hierarchical Galois Key Management Systems for Privacy Preserving AIaaS with Homomorphic Encryption. JW Lee, E Lee, YS Kim, JS No IACR Cryptol. ePrint Arch. 2022, 532, 2022 | 6 | 2022 |
| Analysis of error dependencies on NewHope M Song, S Lee, DJ Shin, E Lee, YS Kim, JS No IEEE Access 8, 45443-45456, 2020 | 6 | 2020 |
| Vehicle and method of controlling the same S Kim, ES Lee, WJ Lee, CM Cho, JS No, YS Kim US Patent 10,407,025, 2019 | 5 | 2019 |
| Powerformer: Efficient privacy-preserving transformer with batch rectifier-power max function and optimized homomorphic attention D Park, E Lee, JW Lee Cryptology ePrint Archive, 2024 | 4 | 2024 |
| Encryption method and apparatus using homomorphic encryption JS No, LEE Joonwoo, YS Kim, Y Lee, E Lee US Patent App. 17/218,790, 2021 | 2 | 2021 |
| Encryption method and apparatus based on homomorphic encryption using composition of functions JS No, YW Lee, E Lee, JW Lee, YS Kim US Patent 11,558,172, 2023 | 1 | 2023 |
| Analysis of Research Trends in Homomorphic Encryption Using Bibliometric Analysis A Yamada, E Lee Journal of the Korea Institute of Information Security & Cryptology 33 (4 …, 2023 | 1 | 2023 |
| Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Convolutions E Lee, JW Lee, J Lee, YS Kim, Y Kim, JS No, W Choi Proc. Int. Conf. Machine Learning (ICML), 12403-12422, 0 | 1 | |
| Optimizing Layerwise Polynomial Approximation for Efficient Private Inference on Fully Homomorphically Encryption: A Dynamic Programming Approach J Lee, E Lee, YS Kim, Y Lee, JW Lee, Y Kim, JS No | 1 | |
