Joon-Woo Lee
Also known as 이준우
Joon-Woo Lee is a researcher in privacy-preserving machine learning whose work lets neural networks run on encrypted data. A doctoral student of Jong-Seon No at Seoul National University, he was first author of a 2021 paper that ran a standard ResNet-20 on the RNS-CKKS homomorphic encryption scheme with bootstrapping, and of the high-precision bootstrapping algorithm that his team presented at Eurocrypt 2021. In 2022, he co-authored a paper at the 39th International Conference on Machine Learning that cut ResNet-20 inference latency 4.67 times compared with earlier work, and he completed his PhD in August 2022. He joined Chung-Ang University in September 2022 and is an assistant professor in its School of Computer Science and Engineering. In 2025, he co-authored Powerformer, a homomorphic-encryption language model published at ACL 2025.
- Now at
- Chung-Ang University
- Born
- 1993
Years link to their sources.
Timeline
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June 2021
ResNet-20 on encrypted images
In June 2021, he is first author, with ten co-authors including Yongwoo Lee, Eunsang Lee and Jong-Seon No, of a paper that implements the standard ResNet-20 on the RNS-CKKS homomorphic encryption scheme with bootstrapping. The encrypted model classifies CIFAR-10 images with 90.67% accuracy and gives 98.67% identical results to the original ResNet-20 on unencrypted data.
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October 2021
High-precision bootstrapping at Eurocrypt
As a PhD student on Jong-Seon No's team at Seoul National University, he is first author of the high-precision bootstrapping algorithm for CKKS homomorphic encryption that the team presents at Eurocrypt 2021 in October 2021, with Eunsang Lee, Yongwoo Lee and Young-Sik Kim. The method raises the accuracy of ciphertext computation by up to 1,000 times and, in collaboration with Samsung Advanced Institute of Technology, supports ResNet-20 classification of homomorphically encrypted images.
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2022
Multiplexed parallel convolutions
In 2022, he co-authors a paper at the 39th International Conference on Machine Learning with Eunsang Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No and Woosuk Choi. Multiplexed parallel convolutions give ResNet-20 on RNS-CKKS 4.67x lower inference latency and 134x less amortized runtime than earlier work, and the team runs ResNet-110 on RNS-CKKS for the first time.
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July 2022
A talk at KAIST
In July 2022, while a doctoral candidate in electrical and computer engineering at Seoul National University, he gives a KAIST seminar titled "Privacy-Preserving Convolutional Deep Neural Networks on the Fully Homomorphic Encryption". The talk covers CKKS and RNS-CKKS with high-precision bootstrapping and privacy-preserving ResNet and VGGNet on CIFAR and ImageNet. He holds a B.S. in electrical and computer engineering from Seoul National University.
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August 2022
Doctorate from Seoul National University
He completes his PhD in the Department of Electrical and Computer Engineering at Seoul National University in August 2022, advised by Jong-Seon No. His graduate work centers on primitives in fully homomorphic encryption, advanced FHE algorithms and efficient privacy-preserving machine learning with deep neural networks.
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Since September 2022
Assistant professor at Chung-Ang
In September 2022, he joins Chung-Ang University as an assistant professor in the School of Computer Science and Engineering, with research on privacy-preserving machine learning using cryptographic tools. In 2024, he and Joohee Lee write Homomorphic Encryption with Authority, a framework for CKKS-based privacy-preserving services that adds a legally authorized authority party.
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2025
Powerformer at ACL 2025
In 2025, he co-authors Powerformer with Dongjin Park and Eunsang Lee, a homomorphic-encryption language model published in the proceedings of ACL 2025. Its BERT-base version cuts computation time by 45% against the previous best HE-based private language model, with a distillation step that swaps softmax and layer normalization for power and linear functions.
Career
- Since 2022Chung-Ang UniversityAssistant Professor
Education
- 2022Seoul National UniversityB.S.
Sources and credits
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- [2106.07229] Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
- Development of a High-Precision Encryption Technology to Ensure Data Privacy in Artificial Intelligence
- High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine Function
- Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions
- 이준우 박사과정 (서울대), 7/11 월요일 4:00 p.m., Privacy-Preserving Convolutional Deep Neural Networks on the Fully Homomorphic Encryption - KAIST 전기 및 전자공학부
- PAICLAB
- Homomorphic Encryption with Authority
- Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption