Ergute Bao (Bob)

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I work on privacy-preserving machine learning. I obtained my PhD from National University of Singapore, supervised by Xiaokui Xiao, and BSc from the Chinese University of Hong Kong.

Publications:

• 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), 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), 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.

Awards:

• Third place in the 2018 Differential Privacy Synthetic Data Challenge
  National Institute of Standards and Technology (NIST)
• First place in the 2020 Privacy Temporal Map Challenge
  National Institute of Standards and Technology (NIST)
• Dean's Graduate Research Excellence Award 2023, Research Achievement Award 2021 & 2022
  NUS School of Computing

Services:

• Conference reviewer in: International Conference on Very Large Data Bases (VLDB) 2026, International Conference on Machine Learning (ICML) 2022, 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: March 2025