Researcher

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.

Born
1993

Years link to their sources.

Timeline

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  1. Since 2022Chung-Ang UniversityAssistant Professor

Education

  1. 2022Seoul National UniversityB.S.

Sources and credits

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  1. [2106.07229] Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network arxiv.org. Checked 21 October 2026.
  2. Development of a High-Precision Encryption Technology to Ensure Data Privacy in Artificial Intelligence webzine-eng.snu.ac.kr. Checked 21 October 2026.
  3. High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine Function eprint.iacr.org. Checked 21 October 2026.
  4. Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions proceedings.mlr.press. Checked 21 October 2026.
  5. 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 ee.kaist.ac.kr. Checked 21 October 2026.
  6. PAICLAB sites.google.com. Checked 21 October 2026.
  7. Homomorphic Encryption with Authority eprint.iacr.org. Checked 21 October 2026.
  8. Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption aclanthology.org. Checked 21 October 2026.