How Does Code Obfuscation Impact Energy Usage? - Cagri Sahin; Philip Tornquist; Ryan McKenna; Zachary Pearson; James Clause (ICSME, 2014)

Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters. - Ryan McKenna; Stephen Herbein; Adam Moody; Todd Gamblin; Michela Taufer (CLUSTER, 2016)

How does code obfuscation impact energy usage? - Cagri Sahin; Mian Wan; Philip Tornquist; Ryan McKenna; Zachary Pearson; William G. J. Halfond; James Clause (J. Softw. Evol. Process., 2016)

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. - Garrett Bernstein; Ryan McKenna; Tao Sun 008; Daniel Sheldon; Michael Hay; Gerome Miklau (ICML, 2017)

EKTELO: A Framework for Defining Differentially-Private Computations. - Dan Zhang; Ryan McKenna; Ios Kotsogiannis; Michael Hay; Ashwin Machanavajjhala; Gerome Miklau (SIGMOD Conference, 2018)

Graphical-model based estimation and inference for differential privacy. - Ryan McKenna; Daniel Sheldon; Gerome Miklau (ICML, 2019)

PSynDB: Accurate and Accessible Private Data Generation. - Zhiqi Huang; Ryan McKenna; George Bissias; Gerome Miklau; Michael Hay; Ashwin Machanavajjhala (Proc. VLDB Endow., 2019)

#8712;: A Framework for Defining Differentially-Private Computations. - Dan Zhang; Ryan McKenna; Ios Kotsogiannis; George Bissias; Michael Hay; Ashwin Machanavajjhala; Gerome Miklau (SIGMOD Rec., 2019)

Fair decision making using privacy-protected data. - David Pujol; Ryan McKenna; Satya Kuppam; Michael Hay; Ashwin Machanavajjhala; Gerome Miklau (FAT*, 2020)

Permute-and-Flip: A new mechanism for differentially private selection. - Ryan McKenna; Daniel Sheldon (NeurIPS, 2020)

A workload-adaptive mechanism for linear queries under local differential privacy. - Ryan McKenna; Raj Kumar Maity; Arya Mazumdar; Gerome Miklau (Proc. VLDB Endow., 2020)

ϵKTELO: A Framework for Defining Differentially Private Computations. - Dan Zhang; Ryan McKenna; Ios Kotsogiannis; George Bissias; Michael Hay; Ashwin Machanavajjhala; Gerome Miklau (ACM Trans. Database Syst., 2020)

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

HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. - Ryan McKenna; Gerome Miklau; Michael Hay; Ashwin Machanavajjhala (CoRR, 2021)

Benchmarking Differentially Private Synthetic Data Generation Algorithms. - Yuchao Tao; Ryan McKenna; Michael Hay; Ashwin Machanavajjhala; Gerome Miklau (CoRR, 2021)

Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. - Ryan McKenna; Gerome Miklau; Daniel Sheldon (J. Priv. Confidentiality, 2021)

AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. - Ryan McKenna; Brett Mullins; Daniel Sheldon; Gerome Miklau (Proc. VLDB Endow., 2022)

(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 002 (NeurIPS, 2023)

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; John Keith Rush; H. Brendan McMahan (NeurIPS, 2023)

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; Keith Rush; Brendan McMahan (CoRR, 2023)

(Amplified) Banded Matrix Factorization: A unified approach to private training. - Christopher A. Choquette-Choo; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Keith Rush; Abhradeep Guha Thakurta; Zheng Xu 002 (CoRR, 2023)

Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. - Ryan McKenna; Gerome Miklau; Michael Hay; Ashwin Machanavajjhala (J. Priv. Confidentiality, 2023)

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

Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML. - Ryan McKenna (CoRR, 2024)

Releasing Large-Scale Human Mobility Histograms with Differential Privacy. - Christopher Bian; Albert Cheu; Yannis Guzman; Marco Gruteser; Peter Kairouz; Ryan McKenna; Edo Roth (CoRR, 2024)

Fine-Tuning Large Language Models with User-Level Differential Privacy. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (CoRR, 2024)

Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data. - Christopher Bian; Albert Cheu; Stanislav Chiknavaryan; Zoe Gong; Marco Gruteser; Oliver Guinan; Yannis Guzman; Peter Kairouz; Artem Lagzdin; Ryan McKenna; Grace Ni; Edo Roth; et al. (CoRR, 2024)

Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML. - Ryan McKenna (ICLR, 2025)

Learning with User-Level Differential Privacy Under Fixed Compute Budgets. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (SaTML, 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 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

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

It's My Data Too: Private ML for Datasets with Multi-User Training Examples. - Arun Ganesh; Ryan McKenna; Brendan McMahan; Adam Smith 006; Fan Wu (CoRR, 2025)

Benchmarking Differentially Private Tabular Data Synthesis. - Kai Chen; Xiaochen Li; Chen Gong; Ryan McKenna; Tianhao Wang (CoRR, 2025)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD. - Nikita P. Kalinin; Ryan McKenna; Jalaj Upadhyay; Christoph H. Lampert (CoRR, 2025)

Correlated Noise Mechanisms for Differentially Private Learning. - Krishna Pillutla; Jalaj Upadhyay; Christopher A. Choquette-Choo; Krishnamurthy Dvijotham; Arun Ganesh; Monika Henzinger; Jonathan Katz; Ryan McKenna; H. Brendan McMahan; Keith Rush; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)