Ryan McKenna

According to our database, Ryan McKenna authored at least 23 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data.

Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon

CoRR, 2024

2023

Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy.

Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala

J. Priv. Confidentiality, August, 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

CoRR, 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

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data.

Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau

Proc. VLDB Endow., 2022

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

Benchmarking Differentially Private Synthetic Data Generation Algorithms.

Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau

CoRR, 2021

HDMM: Optimizing error of high-dimensional statistical queries under differential privacy.

Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala

CoRR, 2021

Relaxed Marginal Consistency for Differentially Private Query Answering.

Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

A workload-adaptive mechanism for linear queries under local differential privacy.

Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau

Proc. VLDB Endow., 2020

Permute-and-Flip: A new mechanism for differentially private selection.

Ryan McKenna, Daniel Sheldon

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fair decision making using privacy-protected data.

David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau

Proceedings of the FAT* '20: Conference on Fairness, 2020

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

PSynDB: Accurate and Accessible Private Data Generation.

Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala

Proc. VLDB Endow., 2019

Graphical-model based estimation and inference for differential privacy.

Ryan McKenna, Daniel Sheldon, Gerome Miklau

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

EKTELO: A Framework for Defining Differentially-Private Computations.

Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau

Proceedings of the 2018 International Conference on Management of Data, 2018

2017

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models.

Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

Proceedings of the 34th International Conference on Machine Learning, 2017

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

Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters.

Ryan McKenna, Stephen Herbein, Adam Moody, Todd Gamblin, Michela Taufer

Proceedings of the 2016 IEEE International Conference on Cluster Computing, 2016

2014

How Does Code Obfuscation Impact Energy Usage?

Cagri Sahin, Philip Tornquist, Ryan McKenna, Zachary Pearson, James Clause

Proceedings of the 30th IEEE International Conference on Software Maintenance and Evolution, Victoria, BC, Canada, September 29, 2014