Jonathan R. Ullman

According to our database1, Jonathan R. Ullman authored at least 86 papers between 2010 and 2024.

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Bibliography

2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization.
CoRR, 2024

Differentially Private Medians and Interior Points for Non-Pathological Data.
Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

2023
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models.
Proc. Priv. Enhancing Technol., July, 2023

Metalearning with Very Few Samples Per Task.
CoRR, 2023

Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning.
CoRR, 2023

Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes.
CoRR, 2023

TMI! Finetuned Models Leak Private Information from their Pretraining Data.
CoRR, 2023

A Bias-Variance-Privacy Trilemma for Statistical Estimation.
CoRR, 2023

SNAP: Efficient Extraction of Private Properties with Poisoning.
Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

From Robustness to Privacy and Back.
Proceedings of the International Conference on Machine Learning, 2023

Multitask Learning via Shared Features: Algorithms and Hardness.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Instance-Optimal Differentially Private Estimation.
CoRR, 2022

How to Combine Membership-Inference Attacks on Multiple Updated Models.
CoRR, 2022

A Private and Computationally-Efficient Estimator for Unbounded Gaussians.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Algorithmic Stability for Adaptive Data Analysis.
SIAM J. Comput., 2021

Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy.
J. Priv. Confidentiality, 2021

Manipulation Attacks in Local Differential Privacy.
J. Priv. Confidentiality, 2021

The limits of pan privacy and shuffle privacy for learning and estimation.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation.
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.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Multidimensional Dynamic Pricing for Welfare Maximization.
ACM Trans. Economics and Comput., 2020

Local Differential Privacy for Evolving Data.
J. Priv. Confidentiality, 2020

PCPs and the Hardness of Generating Synthetic Data.
J. Cryptol., 2020

A Primer on Private Statistics.
CoRR, 2020

The power of factorization mechanisms in local and central differential privacy.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Auditing Differentially Private Machine Learning: How Private is Private SGD?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Private Identity Testing for High-Dimensional Distributions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

CoinPress: Practical Private Mean and Covariance Estimation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Private Query Release Assisted by Public Data.
Proceedings of the 37th International Conference on Machine Learning, 2020

Private Mean Estimation of Heavy-Tailed Distributions.
Proceedings of the Conference on Learning Theory, 2020

Efficient Private Algorithms for Learning Large-Margin Halfspaces.
Proceedings of the Algorithmic Learning Theory, 2020

2019
Program for TPDP 2017.
J. Priv. Confidentiality, 2019

Editorial for Volume 9 Issue 2.
J. Priv. Confidentiality, 2019

Make Up Your Mind: The Price of Online Queries in Differential Privacy.
J. Priv. Confidentiality, 2019

Distributed Differential Privacy via Shuffling.
IACR Cryptol. ePrint Arch., 2019

Securely Sampling Biased Coins with Applications to Differential Privacy.
IACR Cryptol. ePrint Arch., 2019

Efficient Private Algorithms for Learning Halfspaces.
CoRR, 2019

The structure of optimal private tests for simple hypotheses.
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Differentially Private Algorithms for Learning Mixtures of Separated Gaussians.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differentially Private Fair Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Privately Learning High-Dimensional Distributions.
Proceedings of the Conference on Learning Theory, 2019

2018
Fingerprinting Codes and the Price of Approximate Differential Privacy.
SIAM J. Comput., 2018

The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy.
J. Priv. Confidentiality, 2018

Computing marginals using MapReduce.
J. Comput. Syst. Sci., 2018

Distributed Differential Privacy via Mixnets.
CoRR, 2018

The Limits of Post-Selection Generalization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Skyline Identification in Multi-Arm Bandits.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2017
An Antifolk Theorem for Large Repeated Games.
ACM Trans. Economics and Comput., 2017

Hardness of Non-Interactive Differential Privacy from One-Way Functions.
IACR Cryptol. ePrint Arch., 2017

Skyline Identification in Multi-Armed Bandits.
CoRR, 2017

Subgaussian Tail Bounds via Stability Arguments.
CoRR, 2017

Technical Perspective: Building a safety net for data reuse.
Commun. ACM, 2017

Tight Lower Bounds for Differentially Private Selection.
Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

The Price of Selection in Differential Privacy.
Proceedings of the 30th Conference on Learning Theory, 2017

Fractional Set Cover in the Streaming Model.
Proceedings of the Approximation, 2017

2016
Query Release via Online Learning.
Encyclopedia of Algorithms, 2016

Answering n<sup>2+o(1)</sup> Counting Queries with Differential Privacy is Hard.
SIAM J. Comput., 2016

When Can Limited Randomness Be Used in Repeated Games?
Theory Comput. Syst., 2016

Between Pure and Approximate Differential Privacy.
J. Priv. Confidentiality, 2016

Strong Hardness of Privacy from Weak Traitor Tracing.
IACR Cryptol. ePrint Arch., 2016

PSI (Ψ): a Private data Sharing Interface.
CoRR, 2016

Some pairs problems.
Proceedings of the 3rd ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond, 2016

Space Lower Bounds for Itemset Frequency Sketches.
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2016

Privacy Odometers and Filters: Pay-as-you-Go Composition.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Computing Marginals Using MapReduce: Keynote talk paper.
Proceedings of the 20th International Database Engineering & Applications Symposium, 2016

2015
Watch and learn: optimizing from revealed preferences feedback.
SIGecom Exch., 2015

Robust Mediators in Large Games.
CoRR, 2015

Inducing Approximately Optimal Flow Using Truthful Mediators.
Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

Private Multiplicative Weights Beyond Linear Queries.
Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

Robust Traceability from Trace Amounts.
Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science, 2015

Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery.
Proceedings of The 28th Conference on Learning Theory, 2015

2014
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring.
CoRR, 2014

Mechanism design in large games: incentives and privacy.
Proceedings of the Innovations in Theoretical Computer Science, 2014

Faster private release of marginals on small databases.
Proceedings of the Innovations in Theoretical Computer Science, 2014

Privately Solving Linear Programs.
Proceedings of the Automata, Languages, and Programming - 41st International Colloquium, 2014

Preventing False Discovery in Interactive Data Analysis Is Hard.
Proceedings of the 55th IEEE Annual Symposium on Foundations of Computer Science, 2014

2013
Privately Releasing Conjunctions and the Statistical Query Barrier.
SIAM J. Comput., 2013

Answering n<sub>{2+o(1)}</sub> counting queries with differential privacy is hard.
Proceedings of the Symposium on Theory of Computing Conference, 2013

Differential privacy for the analyst via private equilibrium computation.
Proceedings of the Symposium on Theory of Computing Conference, 2013

2012
Answering n^{2+o(1)} Counting Queries with Differential Privacy is Hard
CoRR, 2012

Iterative Constructions and Private Data Release.
Proceedings of the Theory of Cryptography - 9th Theory of Cryptography Conference, 2012

Faster Algorithms for Privately Releasing Marginals.
Proceedings of the Automata, Languages, and Programming - 39th International Colloquium, 2012

2011
PCPs and the Hardness of Generating Private Synthetic Data.
Proceedings of the Theory of Cryptography - 8th Theory of Cryptography Conference, 2011

On the zero-error capacity threshold for deletion channels.
Proceedings of the Information Theory and Applications Workshop, 2011

2010
Course Allocation by Proxy Auction.
Proceedings of the Internet and Network Economics - 6th International Workshop, 2010

The price of privately releasing contingency tables and the spectra of random matrices with correlated rows.
Proceedings of the 42nd ACM Symposium on Theory of Computing, 2010


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