Synthetic Data Generation with Differential Privacy via Bayesian Networks. - Ergute Bao; Xiaokui Xiao; Jun Zhao 007; Dongping Zhang; Bolin Ding (J. Priv. Confidentiality, 2021)

CGM: An Enhanced Mechanism for Streaming Data Collectionwith Local Differential Privacy. - Ergute Bao; Yin Yang 001; Xiaokui Xiao; Bolin Ding (Proc. VLDB Endow., 2021)

DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling. - Jianxin Wei 005; Ergute Bao; Xiaokui Xiao; Yin Yang 001 (CCS, 2022)

Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. - Ergute Bao; Yizheng Zhu; Xiaokui Xiao; Yin Yang 001; Beng Chin Ooi; Benjamin Hong Meng Tan; Khin Mi Mi Aung (Proc. VLDB Endow., 2022)

Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting. - Ergute Bao; Dawei Gao; Xiaokui Xiao; Yaliang Li (KDD, 2023)

AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation. - Fei Wei; Ergute Bao; Xiaokui Xiao; Yin Yang 001; Bolin Ding (Proc. VLDB Endow., 2024)

Towards Learning on Vertically Partitioned Data with Distributed Differential Privacy. - Ergute Bao; Fei Wei; Yin Yang 001; Xiaokui Xiao; Tianyu Pang; Chao Du (ICDE, 2025)

GCON: Differentially Private Graph Convolutional Network via Objective Perturbation. - Jianxin Wei 005; Yizheng Zhu; Xiaokui Xiao; Ergute Bao; Yin Yang 001; Kuntai Cai; Beng Chin Ooi (ICDE, 2025)

Unlocking the Power of Differentially Private Zeroth-order Optimization for Fine-tuning LLMs. - Ergute Bao; Yangfan Jiang 001; Fei Wei; Xiaokui Xiao; Zitao Li; Yaliang Li; Bolin Ding (USENIX Security Symposium, 2025)