@article{lee2025direct,title={Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning},author={Lee, Hong kyu and Liu, Ruixuan and Xiong, Li},journal={arXiv preprint},year={2025}}
ArXiv
Sharpness-Aware Parameter Selection for Machine Unlearning
@article{malekmohammadi2025sharpness,title={Sharpness-Aware Parameter Selection for Machine Unlearning},author={Malekmohammadi, Saber and Lee, Hong kyu and Xiong, Li},journal={arXiv preprint},year={2025}}
ArXiv
Node-level Contrastive Unlearning on Graph Neural Networks
Hong kyu Lee, Qiuchen Zhang, Carl Yang, Li Xiong, and others
@article{lee2025node,title={Node-level Contrastive Unlearning on Graph Neural Networks},author={Lee, Hong kyu and Zhang, Qiuchen and Yang, Carl and Xiong, Li and others},journal={arXiv preprint},year={2025}}
@article{lee2025contrastive,title={Contrastive unlearning: A Contrastive Approach to Machine Unlearning},author={Lee, Hong kyu and Zhang, Qiuchen and Yang, Carl and Lou, Jian and Xiong, Li},journal={The 34th International Joint Conference on Artificial Intelligence (IJCAI)},year={2025}}
@inproceedings{liu2024patient,title={Patient-Centered and Practical Privacy to Support AI for Healthcare},author={Liu, Ruixuan and Lee, Hong Kyu and Bhavani, Sivasubramanium V and Jiang, Xiaoqian and Ohno-Machado, Lucila and Xiong, Li},booktitle={2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)},pages={265--272},year={2024},organization={IEEE}}
ACM WEB 2024
DPAR: Decoupled Graph Neural Networks with Node-level Differential Privacy
Qiuchen Zhang, Hong kyu Lee, Jing Ma, Jian Lou, Carl Yang, and Li Xiong
In Proceedings of the ACM Web Conference 2024, 2024
@inproceedings{zhang2024dpar,title={DPAR: Decoupled Graph Neural Networks with Node-level Differential Privacy},author={Zhang, Qiuchen and Lee, Hong kyu and Ma, Jing and Lou, Jian and Yang, Carl and Xiong, Li},booktitle={Proceedings of the ACM Web Conference 2024},pages={1170--1181},url={https://dl.acm.org/doi/abs/10.1145/3589334.3645531},year={2024}}
2021
Digestive neural networks: A Novel Defense Strategy Against Inference Attacks in Federated Learning
Hong kyu Lee, Jeehyeong Kim, Seyoung Ahn, Rasheed Hussain, Sunghyun Cho, and Junggab Son
@article{lee2021digestive,title={Digestive neural networks: A Novel Defense Strategy Against Inference Attacks in Federated Learning},author={Lee, Hong kyu and Kim, Jeehyeong and Ahn, Seyoung and Hussain, Rasheed and Cho, Sunghyun and Son, Junggab},journal={computers \& security},volume={109},pages={102378},year={2021},publisher={Elsevier},url={https://www.sciencedirect.com/science/article/pii/S0167404821002029},}
On defensive neural networks against inference attack in federated learning
Hong kyu Lee, Jeehyeong Kim, Rasheed Hussain, Sunghyun Cho, and Junggab Son
In ICC 2021-IEEE international conference on communications, 2021
@inproceedings{lee2021defensive,title={On defensive neural networks against inference attack in federated learning},author={Lee, Hong kyu and Kim, Jeehyeong and Hussain, Rasheed and Cho, Sunghyun and Son, Junggab},booktitle={ICC 2021-IEEE international conference on communications},pages={1--6},year={2021},url={https://ieeexplore.ieee.org/abstract/document/9500936},organization={IEEE}}
@article{cranfill2021efficient,title={Letters on wave mechanics},author={Einstein, Albert and Schrödinger, Erwin and Planck, Max and Lorentz, Hendrik Antoon and Przibram, Karl},journal={Security and Communication Networks},volume={2021},number={1},pages={4702469},year={1967},publisher={Vision},}