McKenna, Ryan

13 publications

ICLRW 2025 Benchmarking Differentially Private Tabular Data Synthesis Algorithms Kai Chen, Xiaochen Li, Chen Gong, Ryan McKenna, Tianhao Wang

ICLRW 2025 Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? Marika Swanberg, Ryan McKenna, Edo Roth, Albert Cheu, Peter Kairouz

ICML 2025 It’s My Data Too: Private ML for Datasets with Multi-User Training Examples Arun Ganesh, Ryan Mckenna, Hugh Brendan Mcmahan, Adam Smith, Fan Wu

ICML 2025 Scaling Laws for Differentially Private Language Models Ryan Mckenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang

ICLR 2025 Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML Ryan McKenna

ICMLW 2024 Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles, Arun Ganesh, Ryan McKenna, Hugh Brendan McMahan, Nicole Elyse Mitchell, Krishna Pillutla, J Keith Rush

AISTATS 2024 Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon

NeurIPS 2023 (Amplified) Banded Matrix Factorization: A Unified Approach to Private Training Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu

NeurIPS 2023 Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasiia Koloskova, Ryan McKenna, Zachary Charles, John Rush, H. Brendan McMahan

NeurIPS 2021 Relaxed Marginal Consistency for Differentially Private Query Answering Ryan McKenna, Siddhant Pradhan, Daniel R. Sheldon, Gerome Miklau

NeurIPS 2020 Permute-and-Flip: A New Mechanism for Differentially Private Selection Ryan McKenna, Daniel R. Sheldon

ICML 2019 Graphical-Model Based Estimation and Inference for Differential Privacy Ryan Mckenna, Daniel Sheldon, Gerome Miklau

ICML 2017 Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau