Thomas Steinke

Christopher A. Choquette-Choo

Abhradeep Thakurta

CoRR, 2023

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning.

[BibT_eX]

[DOI]

Krishnamurthy Dvijotham

CoRR, 2023

Differentially Private Stream Processing at Scale.

[BibT_eX]

[DOI]

CoRR, 2023

A Bias-Variance-Privacy Trilemma for Statistical Estimation.

[BibT_eX]

[DOI]

CoRR, 2023

Tight Auditing of Differentially Private Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

Faster Differentially Private Convex Optimization via Second-Order Methods.

[BibT_eX]

[DOI]

Arun Ganesh

Mahdi Haghifam

Abhradeep Guha Thakurta

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

Privacy Auditing with One (1) Training Run.

[BibT_eX]

[DOI]

Milad Nasr

Matthew Jagielski

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

Counting Distinct Elements Under Person-Level Differential Privacy.

[BibT_eX]

[DOI]

Alexander Knop

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

Algorithms with More Granular Differential Privacy Guarantees.

[BibT_eX]

[DOI]

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Why Is Public Pretraining Necessary for Private Model Training?

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

Lun Wang

Proceedings of the International Conference on Machine Learning, 2023

2022

Discrete Gaussian for Differential Privacy.

[BibT_eX]

[DOI]

Clément L. Canonne

Gautam Kamath

J. Priv. Confidentiality, 2022

Composition of Differential Privacy & Privacy Amplification by Subsampling.

[BibT_eX]

[DOI]

CoRR, 2022

Debugging Differential Privacy: A Case Study for Privacy Auditing.

[BibT_eX]

[DOI]

CoRR, 2022

Public Data-Assisted Mirror Descent for Private Model Training.

[BibT_eX]

[DOI]

Vinith M. Suriyakumar

Om Thakkar

Abhradeep Thakurta

Proceedings of the International Conference on Machine Learning, 2022

Hyperparameter Tuning with Renyi Differential Privacy.

[BibT_eX]

[DOI]

Nicolas Papernot

Proceedings of the Tenth International Conference on Learning Representations, 2022

A Private and Computationally-Efficient Estimator for Unbounded Gaussians.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Private Hypothesis Selection.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2021

Algorithmic Stability for Adaptive Data Analysis.

[BibT_eX]

[DOI]

SIAM J. Comput., 2021

The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise.

[BibT_eX]

[DOI]

Zeyu Ding

Daniel Kifer

Sayed M. Saghaian N. E.

CoRR, 2021

Privately Learning Subspaces.

[BibT_eX]

[DOI]

Vikrant Singhal

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

Leveraging Public Data for Practical Private Query Release.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation.

[BibT_eX]

[DOI]

Peter Kairouz

Ziyu Liu

Proceedings of the 38th International Conference on Machine Learning, 2021

PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes.

[BibT_eX]

[DOI]

Peter Grünwald

Lydia Zakynthinou

Proceedings of the Conference on Learning Theory, 2021

Evading the Curse of Dimensionality in Unconstrained Private GLMs.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Multi-Central Differential Privacy.

[BibT_eX]

[DOI]

CoRR, 2020

New Oracle-Efficient Algorithms for Private Synthetic Data Release.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Open Problem: Information Complexity of VC Learning.

[BibT_eX]

[DOI]

Lydia Zakynthinou

Proceedings of the Conference on Learning Theory, 2020

Reasoning About Generalization via Conditional Mutual Information.

[BibT_eX]

[DOI]

Lydia Zakynthinou

Proceedings of the Conference on Learning Theory, 2020

2019

Make Up Your Mind: The Price of Online Queries in Differential Privacy.

[BibT_eX]

[DOI]

J. Priv. Confidentiality, 2019

A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract).

[BibT_eX]

[DOI]

Inform. Spektrum, 2019

Towards Instance-Optimal Private Query Release.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Hybrid Approach to Privacy-Preserving Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

2018

A Hybrid Approach to Privacy-Preserving Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Composable and versatile privacy via truncated CDP.

[BibT_eX]

[DOI]

Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

The Limits of Post-Selection Generalization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Calibrating Noise to Variance in Adaptive Data Analysis.

[BibT_eX]

[DOI]

Vitaly Feldman

Proceedings of the Conference On Learning Theory, 2018

2017

Pseudorandomness and Fourier-Growth Bounds for Width-3 Branching Programs.

[BibT_eX]

[DOI]

Salil P. Vadhan

Andrew Wan

Theory Comput., 2017

Subgaussian Tail Bounds via Stability Arguments.

[BibT_eX]

[DOI]

CoRR, 2017

Tight Lower Bounds for Differentially Private Selection.

[BibT_eX]

[DOI]

Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

Generalization for Adaptively-chosen Estimators via Stable Median.

[BibT_eX]

[DOI]

Vitaly Feldman

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Between Pure and Approximate Differential Privacy.

[BibT_eX]

[DOI]

J. Priv. Confidentiality, 2016

Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds.

[BibT_eX]

[DOI]

IACR Cryptol. ePrint Arch., 2016

2015

Pseudorandomness for Read-Once, Constant-Depth Circuits.

[BibT_eX]

[DOI]

Sitan Chen

Salil P. Vadhan

CoRR, 2015

Robust Traceability from Trace Amounts.

[BibT_eX]

[DOI]

Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science, 2015

Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery.

[BibT_eX]

[DOI]

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs.

[BibT_eX]

[DOI]

Electron. Colloquium Comput. Complex., 2014

Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness.

[BibT_eX]

[DOI]

Electron. Colloquium Comput. Complex., 2014

2013

A Rigorous Extension of the Schönhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic.

[BibT_eX]

[DOI]

Raazesh Sainudiin

Reliab. Comput., 2013

Pseudorandomness for Regular Branching Programs via Fourier Analysis.

[BibT_eX]

[DOI]

Omer Reingold

Salil P. Vadhan

Electron. Colloquium Comput. Complex., 2013

2012

Pseudorandomness for Permutation Branching Programs Without the Group Theory.

[BibT_eX]

[DOI]

Electron. Colloquium Comput. Complex., 2012

Hierarchical Heavy Hitters with the Space Saving Algorithm.

[BibT_eX]

[DOI]

Justin Thaler

Michael Mitzenmacher

Proceedings of the 14th Meeting on Algorithm Engineering & Experiments, 2012

2011

Learning Hurdles for Sleeping Experts.

[BibT_eX]

[DOI]

Varun Kanade