Ryan McKenna

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 37

Top Topics

Matrix Factorization

Undirected Graphical Models

Differential Privacy

Statistical Queries

Top Venues

Proc. VLDB Endow.

J. Priv. Confidentiality

Publications

Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth
Releasing Large-Scale Human Mobility Histograms with Differential Privacy. CoRR (2024)
Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR (2024)
Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. AISTATS (2024)
Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR (2024)
Ryan McKenna
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML. CoRR (2024)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS (2023)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR (2023)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality 13 (1) (2023)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS (2023)
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. 15 (11) (2022)
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR (2022)
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
Relaxed Marginal Consistency for Differentially Private Query Answering. CoRR (2021)
Ryan McKenna, Gerome Miklau, Daniel Sheldon
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality 11 (3) (2021)
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR (2021)
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
Relaxed Marginal Consistency for Differentially Private Query Answering. NeurIPS (2021)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR (2021)
Ryan McKenna, Gerome Miklau, Daniel Sheldon
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. CoRR (2021)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
ϵKTELO: A Framework for Defining Differentially Private Computations. ACM Trans. Database Syst. 45 (1) (2020)
Ryan McKenna, Daniel Sheldon
Permute-and-Flip: A new mechanism for differentially private selection. CoRR (2020)
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair decision making using privacy-protected data. FAT* (2020)
Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau
A workload-adaptive mechanism for linear queries under local differential privacy. CoRR (2020)
Ryan McKenna, Daniel R. Sheldon
Permute-and-Flip: A new mechanism for differentially private selection. NeurIPS (2020)
Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau
A workload-adaptive mechanism for linear queries under local differential privacy. Proc. VLDB Endow. 13 (11) (2020)
Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. 12 (12) (2019)
Ryan McKenna, Daniel Sheldon, Gerome Miklau
Graphical-model based estimation and inference for differential privacy. CoRR (2019)
Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair Decision Making using Privacy-Protected Data. CoRR (2019)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
#8712;: A Framework for Defining Differentially-Private Computations. SIGMOD Rec. 48 (1) (2019)
Ryan McKenna, Daniel Sheldon, Gerome Miklau
Graphical-model based estimation and inference for differential privacy. ICML (2019)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. 11 (10) (2018)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference (2018)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Ektelo: A Framework for Defining Differentially-Private Computations. CoRR (2018)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing error of high-dimensional statistical queries under differential privacy. CoRR (2018)
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR (2017)
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML (2017)
Cagri Sahin, Mian Wan, Philip Tornquist, Ryan McKenna, Zachary Pearson, William G. J. Halfond, James Clause
How does code obfuscation impact energy usage? J. Softw. Evol. Process. 28 (7) (2016)
Ryan McKenna, Stephen Herbein, Adam Moody, Todd Gamblin, Michela Taufer
Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters. CLUSTER (2016)
Cagri Sahin, Philip Tornquist, Ryan McKenna, Zachary Pearson, James Clause
How Does Code Obfuscation Impact Energy Usage? ICSME (2014)