E-LIB Suche - Ergebnisse für: Mcmillan, Audra

1

Differentially Private Heavy Hitter Detection using Federat..:

, In: 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML),

Chadha, Karan ; Chen, Junye ; Duchi, John... - p. 512-533 , 2024

Link: https://doi.org/10.1109/..

2

Hiding Among the Clones: A Simple and Nearly Optimal Analys..:

, In: 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS),

Feldman, Vitaly ; McMillan, Audra ; Talwar, Kunal - p. 954-964 , 2022

Link: https://doi.org/10.1109/..

3

Differentially Private Simple Linear Regression:

Alabi, Daniel ; McMillan, Audra ; Sarathy, Jayshree..
Proceedings on Privacy Enhancing Technologies. 2022 (2022) 2 - p. 184-204 , 2022

Link: https://doi.org/10.2478/..

4

Nonparametric Differentially Private Confidence Intervals f..:

Drechsler, Jörg ; Globus-Harris, Ira ; Mcmillan, Audra..
Journal of Survey Statistics and Methodology. 10 (2022) 3 - p. 804-829 , 2022

Link: https://doi.org/10.1093/..

5

The structure of optimal private tests for simple hypothese:

, In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing,

Canonne, Clément L. ; Kamath, Gautam ; McMillan, Audra.. - p. 310-321 , 2019

Link: https://dl.acm.org/doi/1..

6

When is non-trivial estimation possible for graphons and st..:

Smith, Adam ; McMillan, Audra
Information and Inference: A Journal of the IMA. 7 (2017) 2 - p. 169-181 , 2017

Link: https://doi.org/10.1093/..

7

Differentially Private Heavy Hitter Detection using Federat..:

Chadha, Karan ; Chen, Junye ; Duchi, John...
http://arxiv.org/abs/2307.11749. , 2023

Link: http://arxiv.org/abs/230..

8

Samplable Anonymous Aggregation for Private Federated Data ..:

Talwar, Kunal ; Wang, Shan ; McMillan, Audra...
http://arxiv.org/abs/2307.15017. , 2023

Link: http://arxiv.org/abs/230..

9

Mean Estimation with User-level Privacy under Data Heteroge..:

Cummings, Rachel ; Feldman, Vitaly ; McMillan, Audra.
http://arxiv.org/abs/2307.15835. , 2023

Link: http://arxiv.org/abs/230..

10

Controlling privacy loss in sampling schemes: an analysis o..:

Bun, Mark ; Drechsler, Joerg ; Gaboardi, Marco..
M. Bun, J. Drechsler, M. Gaboardi, A. McMillan, J. Sarathy. 2022. "Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling" https://doi.org/10.4230/LIPIcs.FORC.2022.1. , 2023

Link: https://hdl.handle.net/2..

11

Private Federated Statistics in an Interactive Setting:

McMillan, Audra ; Javidbakht, Omid ; Talwar, Kunal...
http://arxiv.org/abs/2211.10082. , 2022

Link: http://arxiv.org/abs/221..

12

Controlling Privacy Loss in Sampling Schemes: An Analysis o..:

Bun, Mark ; Gaboardi, Marco ; McMillan, Audra.
doi:10.4230/LIPIcs.FORC.2022.1. , 2022

Link: https://doi.org/10.4230/..

13

Nonparametric differentially private confidence intervals f..:

Drechsler, Jörg ; Globus-Harris, Ira ; Mcmillan, Audra..
Journal of Survey Statistics and Methodology. , 2022

Link: https://hdl.handle.net/2..

14

Stronger Privacy Amplification by Shuffling for R\'enyi and..:

Feldman, Vitaly ; McMillan, Audra ; Talwar, Kunal
http://arxiv.org/abs/2208.04591. , 2022

Link: http://arxiv.org/abs/220..

15

Differentially private simple linear regression:

Alabi, Daniel ; McMillan, Audra ; Sarathy, Jayshree..
Proceedings on Privacy Enhancing Technologies. , 2022

Link: https://hdl.handle.net/2..