Publications

2026

1. ACL 2026

   Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning

   Hong Kyu Lee, Ruixuan Liu, and Li Xiong

   In Findings of the Association for Computational Linguistics: ACL 2026, 2026

   DOI arXiv Bib HTML

   @inproceedings{lee2026direct,
     title = {Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning},
     author = {Lee, Hong Kyu and Liu, Ruixuan and Xiong, Li},
     booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
     pages = {42083--42100},
     publisher = {Association for Computational Linguistics},
     year = {2026},
     doi = {10.18653/v1/2026.findings-acl.2088},
   }

2025

1. ArXiv

   Sharpness-Aware Parameter Selection for Machine Unlearning

   Saber Malekmohammadi, Hong kyu Lee, and Li Xiong

   arXiv preprint, 2025

   arXiv Bib

   @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}
   }

2. ArXiv

   Node-level Contrastive Unlearning on Graph Neural Networks

   Hong kyu Lee, Qiuchen Zhang, Carl Yang, Li Xiong, and others

   arXiv preprint, 2025

   arXiv Bib

   @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}
   }

3. IJCAI 2025

   Contrastive unlearning: A Contrastive Approach to Machine Unlearning

   Hong kyu Lee, Qiuchen Zhang, Carl Yang, Jian Lou, and Li Xiong

   The 34th International Joint Conference on Artificial Intelligence (IJCAI), 2025

   arXiv Bib Code

   @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}
   }

2024

1. TPS-ISA

   Patient-Centered and Practical Privacy to Support AI for Healthcare

   Ruixuan Liu, Hong Kyu Lee, Sivasubramanium V Bhavani, Xiaoqian Jiang, Lucila Ohno-Machado, and Li Xiong

   In 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), 2024

   Bib

   @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}
   }

2. 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

   Bib Code

   @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

1. 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

   computers & security, 2021

   Bib

   @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},
   }

2. 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

   Bib

   @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}
   }

3. Efficient yet Robust Privacy Preservation for MPEG-DASH-Based Video Streaming

   Luke Cranfill, Jeehyeong Kim, Hong kyu Lee, Victor Youdom Kemmoe, Sunghyun Cho, and Junggab Son

   Security and Communication Networks, 2021

   Bib

   @article{cranfill2021efficient,
     title = {Efficient yet Robust Privacy Preservation for MPEG-DASH-Based Video Streaming},
     author = {Cranfill, Luke and Kim, Jeehyeong and Lee, Hong kyu and Kemmoe, Victor Youdom and Cho, Sunghyun and Son, Junggab},
     journal = {Security and Communication Networks},
     volume = {2021},
     number = {1},
     pages = {4702469},
     year = {2021},
     publisher = {Wiley Online Library},
   }