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Ryan McKenna
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Graphical-model based estimation and inference for differential privacy
R McKenna, D Sheldon, G Miklau
International Conference on Machine Learning, 4435-4444, 2019
1302019
Optimizing error of high-dimensional statistical queries under differential privacy
R McKenna, G Miklau, M Hay, A Machanavajjhala
arXiv preprint arXiv:1808.03537, 2018
1232018
How does code obfuscation impact energy usage?
C Sahin, P Tornquist, R McKenna, Z Pearson, J Clause
2014 IEEE international conference on software maintenance and evolution …, 2014
1152014
Fair decision making using privacy-protected data
D Pujol, R McKenna, S Kuppam, M Hay, A Machanavajjhala, G Miklau
Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020
832020
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
R McKenna, G Miklau, D Sheldon
arXiv preprint arXiv:2108.04978, 2021
792021
Ektelo: A framework for defining differentially-private computations
D Zhang, R McKenna, I Kotsogiannis, M Hay, A Machanavajjhala, ...
Proceedings of the 2018 International Conference on Management of Data, 115-130, 2018
692018
Benchmarking differentially private synthetic data generation algorithms
Y Tao, R McKenna, M Hay, A Machanavajjhala, G Miklau
arXiv preprint arXiv:2112.09238, 2021
682021
Machine learning predictions of runtime and IO traffic on high-end clusters
R McKenna, S Herbein, A Moody, T Gamblin, M Taufer
2016 IEEE International Conference on Cluster Computing (CLUSTER), 255-258, 2016
572016
Permute-and-flip: A new mechanism for differentially private selection
R McKenna, DR Sheldon
Advances in Neural Information Processing Systems 33, 193-203, 2020
532020
Aim: An adaptive and iterative mechanism for differentially private synthetic data
R McKenna, B Mullins, D Sheldon, G Miklau
arXiv preprint arXiv:2201.12677, 2022
342022
Differentially private learning of undirected graphical models using collective graphical models
G Bernstein, R McKenna, T Sun, D Sheldon, M Hay, G Miklau
International Conference on Machine Learning, 478-487, 2017
342017
Fair decision making using privacy-protected data
S Kuppam, R McKenna, D Pujol, M Hay, A Machanavajjhala, G Miklau
arXiv preprint arXiv:1905.12744, 2019
192019
Hdmm: Optimizing error of high-dimensional statistical queries under differential privacy
R McKenna, G Miklau, M Hay, A Machanavajjhala
arXiv preprint arXiv:2106.12118, 2021
162021
(Amplified) Banded Matrix Factorization: A unified approach to private training
CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ...
Advances in Neural Information Processing Systems 36, 2024
102024
Relaxed marginal consistency for differentially private query answering
R McKenna, S Pradhan, DR Sheldon, G Miklau
Advances in Neural Information Processing Systems 34, 20696-20707, 2021
92021
From HPC performance to climate modeling: Transforming methods for HPC predictions into models of extreme climate conditions
R McKinney, VK Pallipuram, R Vargas, M Taufer
2015 IEEE 11th International Conference on e-Science, 108-117, 2015
92015
A workload-adaptive mechanism for linear queries under local differential privacy
R McKenna, RK Maity, A Mazumdar, G Miklau
arXiv preprint arXiv:2002.01582, 2020
72020
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
A Koloskova, R McKenna, Z Charles, J Rush, HB McMahan
Advances in Neural Information Processing Systems 36, 2023
62023
PSynDB: accurate and accessible private data generation
Z Huang, R McKenna, G Bissias, G Miklau, M Hay, A Machanavajjhala
Proceedings of the VLDB Endowment 12 (12), 1918-1921, 2019
62019
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
M Fuentes, B Mullins, R McKenna, G Miklau, D Sheldon
arXiv preprint arXiv:2403.07797, 2024
2024
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