Differentially Private Change-Point Detection. - Rachel Cummings; Sara Krehbiel; Yajun Mei; Rui Tuo; Wanrong Zhang 001 (NeurIPS, 2018)

Privately detecting changes in unknown distributions. - Rachel Cummings; Sara Krehbiel; Yuliia Lut; Wanrong Zhang 001 (ICML, 2020)

Dataset-Level Attribute Leakage in Collaborative Learning. - Wanrong Zhang 001; Shruti Tople; Olga Ohrimenko (CoRR, 2020)

PAPRIKA: Private Online False Discovery Rate Control. - Wanrong Zhang 001; Gautam Kamath 001; Rachel Cummings (ICML, 2021)

Leakage of Dataset Properties in Multi-Party Machine Learning. - Wanrong Zhang 001; Shruti Tople; Olga Ohrimenko (USENIX Security Symposium, 2021)

Single and Multiple Change-Point Detection with Differential Privacy. - Wanrong Zhang 001; Sara Krehbiel; Rui Tuo; Yajun Mei; Rachel Cummings (J. Mach. Learn. Res., 2021)

Privacy-preserving Statistical Tools: Differential Privacy and Beyond. - Wanrong Zhang 001 (2021)

Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size. - Wanrong Zhang 001; Yajun Mei; Rachel Cummings (AISTATS, 2022)

Attribute Privacy: Framework and Mechanisms. - Wanrong Zhang 001; Olga Ohrimenko; Rachel Cummings (FAccT, 2022)

Concurrent Composition Theorems for all Standard Variants of Differential Privacy. - Salil P. Vadhan; Wanrong Zhang 001 (CoRR, 2022)

Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters. - Samuel Haney; Michael Shoemate; Grace Tian; Salil P. Vadhan; Andrew Vyrros; Vicki Xu; Wanrong Zhang 001 (CCS, 2023)

DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. - Wanrong Zhang 001; Ruqi Zhang (ICML, 2023)

Concurrent Composition Theorems for Differential Privacy. - Salil P. Vadhan; Wanrong Zhang 001 (STOC, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

Continual Release of Differentially Private Synthetic Data. - Mark Bun; Marco Gaboardi; Marcel Neunhoeffer; Wanrong Zhang 001 (CoRR, 2023)

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training. - Alyssa Huang; Peihan Liu; Ryumei Nakada; Linjun Zhang; Wanrong Zhang 001 (CoRR, 2023)

Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control. - Wanrong Zhang 001; Yajun Mei (Technometrics, 2023)

Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections. - Mark Bun; Marco Gaboardi; Marcel Neunhoeffer; Wanrong Zhang 001 (Proc. ACM Manag. Data, 2024)

Membership Inference Attacks and Privacy in Topic Modeling. - Nico Manzonelli; Wanrong Zhang 001; Salil P. Vadhan (Trans. Mach. Learn. Res., 2024)

An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses. - Hao Liang; Wanrong Zhang 001; Xinlei He 001; Kaishun Wu; Hong Xing (CoRR, 2025)