Ergute Bao (Bob)

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I am a postdoc researcher in Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE, working with Ting Yu. I work on privacy, with specific interests in differential privacy (DP) for data analysis, federated learning (FL), and large language model (LLM).

Previously, I obtained PhD in CS from National University of Singapore (NUS), supervised by Xiaokui Xiao, in 2024. Previously, I obtained BSc in CS from the Chinese University of Hong Kong (CUHK) in 2018. During my PhD, I have participated in Differential Privacy challenges held by NIST, and our team won the third place in 2018 and the first place in 2020 . I have also enjoyed summers as a student intern in CUHK, SEA AI Lab, and Alibaba Group while my mentors mostly suffered.

Publications: [Google Scholar] [DBLP]

• Accurate Table Question Answering with Accessible LLMs.
  Yangfan Jiang, Fei Wei, E. Bao, Yaliang Li, Bolin Ding, Yin Yang, and Xiaokui Xiao.
  Proceedings of the IEEE International Conference on Data Engineering (ICDE), to appear, 2026.
• Unlocking the Power of Differentially Private Zeroth-order Optimization for Fine-tuning LLMs.
  E. Bao, Yangfan Jiang, Fei Wei, Xiaokui Xiao, Zitao Li, Yaliang Li, and Bolin Ding
  Proceedings of the USENIX Conference on Security Symposium (SEC), to appear, 2025. erratum
• Towards Learning on Vertically Partitioned Data with Distributed Differential Privacy.
  E. Bao, Fei Wei, Xiaokui Xiao, Yin Yang, Tianyu Pang, and Chao Du
  Proceedings of the IEEE International Conference on Data Engineering (ICDE), to appear, 2025.
• GCON: Differentially Private Graph Convolutional Network via Objective Perturbation.
  Jianxin Wei, Yizheng Zhu, Xiaokui Xiao, E. Bao, Yin Yang, Kuntai Cai, and Beng Chin Ooi.
  Proceedings of the IEEE International Conference on Data Engineering (ICDE), to appear, 2025.
• AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
  Fei Wei, E. Bao, Xiaokui Xiao, Yin Yang, and Bolin Ding
  Proceedings of the VLDB Endowment (PVLDB), 2024.
• Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting
  E. Bao, Dawei Gao, Xiaokui Xiao, and Yaliang Li
  Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023.
• Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
  E. Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin H.M. Tan, and Khin M.M. Aung
  Proceedings of the VLDB Endowment (PVLDB), 2022.
• DPIS: an Enhanced Mechanism for Differentially Private SGD with Importance Sampling
  Jianxin Wei, E. Bao, Xiaokui Xiao, and Yin Yang
  Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), 2022.
• CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy
  E. Bao, Yin Yang, Xiaokui Xiao, and Bolin Ding
  Proceedings of the VLDB Endowment (PVLDB), 2021.
• Synthetic Data Generation with Differential Privacy via Bayesian Networks
  E. Bao, Xiaokui Xiao, Jun Zhao, Dongping Zhang, and Bolin Ding
  Journal of Privacy and Confidentiality (JPC), 2021, 11(3). Invited paper, based on our solution for 2018 NIST DP challenge.

Services:

• Program committee member in: International Conference on Very Large Data Bases (VLDB) 2026, IEEE International Conference on Data Engineering (ICDE) 2026, International Conference on Database Systems for Advanced Applications (DASFAA) 2023-24 (Demo track).
• Journal reviewer in: IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Privacy (ToP), Springer International Journal of Information Security (IJIS).

Contact:

Email: baoergute8@gmail.com

Last updated: June 2025