Jonathan R. Ullman

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 120

Top Venues

CoRR

J. Priv. Confidentiality

NeurIPS

STOC

Publications

Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS (2024)
Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR (2024)
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. 2023 (3) (2023)
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou
Multitask Learning via Shared Features: Algorithms and Hardness. COLT (2023)
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman
Metalearning with Very Few Samples Per Task. CoRR (2023)
Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou
From Robustness to Privacy and Back. ICML (2023)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. SP (2023)
Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. CoRR (2023)
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR (2023)
Naty Peter, Eliad Tsfadia, Jonathan R. Ullman
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. CoRR (2023)
John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman
TMI! Finetuned Models Leak Private Information from their Pretraining Data. CoRR (2023)
Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou
From Robustness to Privacy and Back. CoRR (2023)
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR (2023)
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou
Multitask Learning via Shared Features: Algorithms and Hardness. CoRR (2022)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT (2022)
Audra McMillan, Adam D. Smith, Jonathan R. Ullman
Instance-Optimal Differentially Private Estimation. CoRR (2022)
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR (2022)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. SIAM J. Comput. 50 (3) (2021)
Adam Sealfon, Jonathan R. Ullman
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. J. Priv. Confidentiality 11 (1) (2021)
Albert Cheu, Jonathan R. Ullman
The limits of pan privacy and shuffle privacy for learning and estimation. STOC (2021)
Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. CoRR (2021)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. IEEE Symposium on Security and Privacy (2021)
Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. NeurIPS (2021)
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. CoRR (2021)
Konstantina Bairaktari, Huy Le Nguyen, Jonathan R. Ullman
Fair and Optimal Cohort Selection for Linear Utilities. CoRR (2021)
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. ICML (2021)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11 (1) (2021)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR (2021)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8 (1) (2020)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10 (1) (2020)
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu
Private Query Release Assisted by Public Data. ICML (2020)
Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR (2020)
Albert Cheu, Jonathan R. Ullman
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation. CoRR (2020)
Matthew Jagielski, Jonathan R. Ullman, Alina Oprea
Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS (2020)
Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
CoinPress: Practical Private Mean and Covariance Estimation. CoRR (2020)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. CoRR (2020)
Jonathan R. Ullman, Salil P. Vadhan
PCPs and the Hardness of Generating Synthetic Data. J. Cryptol. 33 (4) (2020)
Gautam Kamath, Jonathan R. Ullman
A Primer on Private Statistics. CoRR (2020)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. COLT (2020)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
Private Identity Testing for High-Dimensional Distributions. NeurIPS (2020)
Huy Le Nguyen, Jonathan R. Ullman, Lydia Zakynthinou
Efficient Private Algorithms for Learning Large-Margin Halfspaces. ALT (2020)
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu
Private Query Release Assisted by Public Data. CoRR (2020)
Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
CoinPress: Practical Private Mean and Covariance Estimation. NeurIPS (2020)
Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman
The power of factorization mechanisms in local and central differential privacy. STOC (2020)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA (2020)
Mark Bun, Thomas Steinke, Jonathan R. Ullman
Make Up Your Mind: The Price of Online Queries in Differential Privacy. J. Priv. Confidentiality 9 (1) (2019)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
Private Identity Testing for High-Dimensional Distributions. CoRR (2019)
Adam Sealfon, Jonathan R. Ullman
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. CoRR (2019)
Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman
Securely Sampling Biased Coins with Applications to Differential Privacy. IACR Cryptol. ePrint Arch. 2019 (2019)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR (2019)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Shuffling. EUROCRYPT (1) (2019)
Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman
Securely Sampling Biased Coins with Applications to Differential Privacy. CCS (2019)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Shuffling. IACR Cryptol. ePrint Arch. 2019 (2019)
Huy L. Nguyen, Jonathan R. Ullman, Lydia Zakynthinou
Efficient Private Algorithms for Learning Halfspaces. CoRR (2019)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. CoRR (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The structure of optimal private tests for simple hypotheses. STOC (2019)
Jonathan R. Ullman, Lars Vilhuber
Program for TPDP 2017. J. Priv. Confidentiality 9 (2) (2019)
Jonathan R. Ullman, Adam Sealfon
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. NeurIPS (2019)
Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman
The Power of Factorization Mechanisms in Local and Central Differential Privacy. CoRR (2019)
Jonathan R. Ullman, Lars Vilhuber
Editorial for Volume 9 Issue 2. J. Priv. Confidentiality 9 (2) (2019)
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
Privately Learning High-Dimensional Distributions. COLT (2019)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. ICML (2019)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. NeurIPS (2018)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR (2018)
Albert Cheu, Ravi Sundaram, Jonathan R. Ullman
Skyline Identification in Multi-Arm Bandits. ISIT (2018)
Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
The Limits of Post-Selection Generalization. NeurIPS (2018)
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
The Limits of Post-Selection Generalization. CoRR (2018)
Mark Bun, Jonathan R. Ullman, Salil P. Vadhan
Fingerprinting Codes and the Price of Approximate Differential Privacy. SIAM J. Comput. 47 (5) (2018)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. CoRR (2018)
Cynthia Dwork, Jonathan R. Ullman
The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy. J. Priv. Confidentiality 8 (1) (2018)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. CoRR (2018)
Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs
Hardness of Non-interactive Differential Privacy from One-Way Functions. CRYPTO (1) (2018)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Mixnets. CoRR (2018)
Foto N. Afrati, Shantanu Sharma, Jonathan R. Ullman, Jeffrey D. Ullman
Computing marginals using MapReduce. J. Comput. Syst. Sci. 94 (2018)
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
Privately Learning High-Dimensional Distributions. CoRR (2018)
Mitali Bafna, Jonathan R. Ullman
The Price of Selection in Differential Privacy. CoRR (2017)
Mark Bun, Thomas Steinke, Jonathan R. Ullman
Make Up Your Mind: The Price of Online Queries in Differential Privacy. SODA (2017)
Mitali Bafna, Jonathan R. Ullman
The Price of Selection in Differential Privacy. COLT (2017)
Thomas Steinke, Jonathan R. Ullman
Tight Lower Bounds for Differentially Private Selection. FOCS (2017)
Jonathan R. Ullman
Technical Perspective: Building a safety net for data reuse. Commun. ACM 60 (4) (2017)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. EC (2017)
Thomas Steinke, Jonathan R. Ullman
Subgaussian Tail Bounds via Stability Arguments. CoRR (2017)
Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs
Hardness of Non-Interactive Differential Privacy from One-Way Functions. IACR Cryptol. ePrint Arch. 2017 (2017)
Thomas Steinke, Jonathan R. Ullman
Tight Lower Bounds for Differentially Private Selection. CoRR (2017)
Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman
An Antifolk Theorem for Large Repeated Games. ACM Trans. Economics and Comput. 5 (2) (2017)
Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan R. Ullman, Ali Vakilian, Anak Yodpinyanee
Fractional Set Cover in the Streaming Model. APPROX-RANDOM (2017)
Albert Cheu, Ravi Sundaram, Jonathan R. Ullman
Skyline Identification in Multi-Armed Bandits. CoRR (2017)
Thomas Steinke, Jonathan R. Ullman
Interactive fingerprinting codes and the hardness of preventing false discovery. ITA (2016)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic stability for adaptive data analysis. STOC (2016)
Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry
Strong Hardness of Privacy from Weak Traitor Tracing. IACR Cryptol. ePrint Arch. 2016 (2016)
Edo Liberty, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman
Space Lower Bounds for Itemset Frequency Sketches. PODS (2016)
Foto N. Afrati, Shantanu Sharma, Jeffrey D. Ullman, Jonathan R. Ullman
Computing Marginals Using MapReduce: Keynote talk paper. IDEAS (2016)
Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
Watch and learn: optimizing from revealed preferences feedback. STOC (2016)
Jeffrey D. Ullman, Jonathan R. Ullman
Some pairs problems. BeyondMR@SIGMOD (2016)
Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan R. Ullman, Salil P. Vadhan
PSI (Ψ): a Private data Sharing Interface. CoRR (2016)
Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Salil P. Vadhan
Privacy Odometers and Filters: Pay-as-you-Go Composition. CoRR (2016)
Mark Bun, Thomas Steinke, Jonathan R. Ullman
Make Up Your Mind: The Price of Online Queries in Differential Privacy. CoRR (2016)
Jonathan R. Ullman
Counting Queries with Differential Privacy is Hard. SIAM J. Comput. 45 (2) (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jonathan R. Ullman