Research Papers
I am committed to making all of my papers freely available as quickly as possible by hosting them on arXiv or similar open access preprint servers, and these preprints should be considered the authoritative version of the paper. When possible I will also link to any associated code, data, talks, or expository writing available. These resources are indicated with paper code talk misc tags.
I prefer not to order authors by contribution, but sometimes the norms of the community and the needs of students demand it. Author ordering is alphabetical except where otherwise noted.
Manuscripts
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
arXiv
John Abascal, Nicolás Berrios, Alina Oprea, Jonathan Ullman, Adam Smith, and Matthew Jagielski (contribution order)
John Abascal, Nicolás Berrios, Alina Oprea, Jonathan Ullman, Adam Smith, and Matthew Jagielski (contribution order)
Instance-Optimal Differentially Private Estimation
arXiv
Audra McMillan, Adam Smith, and Jonathan Ullman
Audra McMillan, Adam Smith, and Jonathan Ullman
Conference and other Primary Publications
Like most computer scientists, my work is primarily published in competitive conferences, which are typically as selective as top journals. This list includes all primary publications of my work in regardless of venue.
Privacy in Metalearning and Multitask Learning: Modeling and Separations
arXiv
Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, and Jonathan Ullman International Conference on Artificial Intelligence and Statistics (AISTATS '25)
Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, and Jonathan Ullman International Conference on Artificial Intelligence and Statistics (AISTATS '25)
A Bias-Variance-Privacy Trilemma for Statistical Estimation
arXiv
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman Journal of the American Statistical Association (JASA '25)
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman Journal of the American Statistical Association (JASA '25)
Private Mean Estimation with Person-Level Differential Privacy
arXiv
Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, and Jonathan Ullman ACM-SIAM Symposium on Discrete Algorithms (SODA '25)
Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, and Jonathan Ullman ACM-SIAM Symposium on Discrete Algorithms (SODA '25)
Private Geometric Median
arXiv
Mahdi Haghifam, Thomas Steinke, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '24)
Mahdi Haghifam, Thomas Steinke, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '24)
Metalearning with Very Few Samples Per Task
arXiv
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, and Jonathan Ullman
Conference on Learning Theory (COLT '24)
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, and Jonathan Ullman
Conference on Learning Theory (COLT '24)
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
arXiv
Naty Peter, Eliad Tsfadia, and Jonathan Ullman
Conference on Learning Theory (COLT '24)
Naty Peter, Eliad Tsfadia, and Jonathan Ullman
Conference on Learning Theory (COLT '24)
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
arXiv
Andrew Lowy, Jonathan Ullman, and Stephen Wright
International Conference on Machine Learning (ICML '24)
Andrew Lowy, Jonathan Ullman, and Stephen Wright
International Conference on Machine Learning (ICML '24)
Program Analysis for Adaptive Data Analysis
Jiawen Liu, Weihao Qu, Marco Gaboardi, Deepak Garg, and Jonathan Ullman (contribution order)
Conference on Programming Languages Design and Implementation (PLDI '24)
Jiawen Liu, Weihao Qu, Marco Gaboardi, Deepak Garg, and Jonathan Ullman (contribution order)
Conference on Programming Languages Design and Implementation (PLDI '24)
TMI! Finetuned Models Leak Private Information from their Pretraining Data
arXiv
John Abascal, Stanley Wu, Alina Oprea, and Jonathan Ullman (contribution order)
Privacy Enhancing Technologies Symposium (PETS '24)
John Abascal, Stanley Wu, Alina Oprea, and Jonathan Ullman (contribution order)
Privacy Enhancing Technologies Symposium (PETS '24)
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
arXiv GitHub Video
Harsh Chaudhuri, Giorgio Severi, Alina Oprea, and Jonathan Ullman (contribution order)
International Conference on Learning Representations (ICLR '24)
Harsh Chaudhuri, Giorgio Severi, Alina Oprea, and Jonathan Ullman (contribution order)
International Conference on Learning Representations (ICLR '24)
Differentially Private Medians and Interior Points for Non-Pathological Data
arXiv
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, and Jonathan Ullman
Innovations in Theoretical Computer Science (ITCS '24)
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, and Jonathan Ullman
Innovations in Theoretical Computer Science (ITCS '24)
Fair and Useful Cohort Selection
arXiv
Konstantina Bairaktari, Paul Langton, Huy Le Nguyen, Niklas Smedemark-Margulies, and Jonathan Ullman
Transactions on Machine Learning Research (TMLR '23)
Konstantina Bairaktari, Paul Langton, Huy Le Nguyen, Niklas Smedemark-Margulies, and Jonathan Ullman
Transactions on Machine Learning Research (TMLR '23)
Visual Utility Evaluation of Differentially Private Scatterplots
IEEE Xplore
Liudas Panavas, Tarik Crnovrsanin, Jane L. Adams, Ali Sarvghad, Melanie Tory, Jonathan Ullman, and Cody Dunne (contribution order)
IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG '23)
Liudas Panavas, Tarik Crnovrsanin, Jane L. Adams, Ali Sarvghad, Melanie Tory, Jonathan Ullman, and Cody Dunne (contribution order)
IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG '23)
Multitask Learning via Shared Features: Algorithms and Hardness
arXiv
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan Ullman, and Lydia Zakynthinou
Conference on Learning Theory (COLT '23)
Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan Ullman, and Lydia Zakynthinou
Conference on Learning Theory (COLT '23)
From Robustness to Privacy and Back
arXiv
Hilal Asi, Jonathan Ullman, and Lydia Zakynthinou
International Conference on Machine Learning (ICML '23)
Hilal Asi, Jonathan Ullman, and Lydia Zakynthinou
International Conference on Machine Learning (ICML '23)
How to Combine Membership-Inference Attacks on Multiple Updated Models
arXiv
Matthew Jagielski*, Stanley Wu*, Alina Oprea, Jonathan Ullman, and Roxana Geambasu (contribution order)
Privacy Enhancing Technologies Symposium (PETS '23)
Matthew Jagielski*, Stanley Wu*, Alina Oprea, Jonathan Ullman, and Roxana Geambasu (contribution order)
Privacy Enhancing Technologies Symposium (PETS '23)
SNAP: Efficient Extraction of Private Properties with Poisoning
arXiv GitHub 15m Video
Harsh Chaudhuri, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, and Jonathan Ullman (contribution order)
IEEE Symposium on Security and Privacy (IEEE S&P'23)
Harsh Chaudhuri, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, and Jonathan Ullman (contribution order)
IEEE Symposium on Security and Privacy (IEEE S&P'23)
A Private and Computationally Efficient Estimator for Unbounded Gaussians
arXiv
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman
Conference on Learning Theory (COLT '22)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman
Conference on Learning Theory (COLT '22)
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
arXiv
Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou
Conference on Neural and Information Processing Systems (NeurIPS '21)
Selected as a Spotlight Presentation
Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthinou
Conference on Neural and Information Processing Systems (NeurIPS '21)
Selected as a Spotlight Presentation
Leveraging Public Data for Practical Private Query Release
arXiv
60m Video
Terrance Liu, Thomas Steinke, Jonathan Ullman, Giuseppi Vietri, and Zhiwei Steven Wu
International Conference on Machine Learning (ICML '21)
Terrance Liu, Thomas Steinke, Jonathan Ullman, Giuseppi Vietri, and Zhiwei Steven Wu
International Conference on Machine Learning (ICML '21)
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation
arXiv
60m Video
Albert Cheu and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '21)
Albert Cheu and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '21)
Manipulation Attacks in Local Differential Privacy
arXiv 1m Video
Albert Cheu, Adam Smith, and Jonathan Ullman
IEEE Symposium on Security and Privacy (IEEE S&P '21)
Albert Cheu, Adam Smith, and Jonathan Ullman
IEEE Symposium on Security and Privacy (IEEE S&P '21)
Auditing Differentially Private Machine Learning: How Private is Private SGD? arXiv GitHub 15m Video
Matthew Jagielski, Jonathan Ullman, and Alina Oprea (contribution order)
Conference on Neural and Information Processing Systems (NeurIPS '20)
Matthew Jagielski, Jonathan Ullman, and Alina Oprea (contribution order)
Conference on Neural and Information Processing Systems (NeurIPS '20)
CoinPress: Practical Private Mean and Covariance Estimation arXiv GitHub
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '20)
Sourav Biswas, Yihe Dong, Gautam Kamath, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '20)
Private Identity Testing for High-Dimensional Distributions
arXiv
Clément Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, and Lydia Zakynthinou
Conference on Neural and Information Processing Systems (NeurIPS '20)
Selected as a Spotlight Presentation
Clément Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, and Lydia Zakynthinou
Conference on Neural and Information Processing Systems (NeurIPS '20)
Selected as a Spotlight Presentation
Private Query Release Assisted by Public Data
arXiv 15m Video
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Zhiwei Steven Wu
International Conference on Machine Learning (ICML '20)
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Zhiwei Steven Wu
International Conference on Machine Learning (ICML '20)
Private Mean Estimation of Heavy-Tailed Distributions
arXiv
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman
Conference on Learning Theory (COLT '20)
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman
Conference on Learning Theory (COLT '20)
The Power of Factorization Mechanisms in Local and Central Differential Privacy
arXiv
Alexander Edmonds, Aleksandar Nikolov, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '20)
Alexander Edmonds, Aleksandar Nikolov, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '20)
Efficient Private Algorithms for Learning Halfspaces
arXiv
Huy Lê Nguyễn, Jonathan Ullman, and Lydia Zakynthinou
Conference on Algorithmic Learning Theory (ALT '20)
Huy Lê Nguyễn, Jonathan Ullman, and Lydia Zakynthinou
Conference on Algorithmic Learning Theory (ALT '20)
Differentially Private Algorithms for Learning Mixtures of Gaussians
arXiv
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '19)
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '19)
Efficiently Estimating Erdős-Rényi Graphs with Differential Privacy
arXiv
Poster
Adam Sealfon and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '19)
Adam Sealfon and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '19)
Securely Sampling Biased Coins with Applications to Differential Privacy
ePrint
Jeffrey Champion, abhi shelat, and Jonathan Ullman
ACM Conference on Computer Security (CCS '19)
Jeffrey Champion, abhi shelat, and Jonathan Ullman
ACM Conference on Computer Security (CCS '19)
Differentially Private Fair Learning
arXiv
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman
International Conference on Machine Learning (ICML '19)
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman
International Conference on Machine Learning (ICML '19)
Privately Learning High-Dimensional Distributions
arXiv
Talk Video
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman
Conference on Learning Theory (COLT '19)
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman
Conference on Learning Theory (COLT '19)
The Structure of Optimal Private Tests for Simple Hypotheses
arXiv
Talk Video
Clément Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '19)
Clément Canonne, Gautam Kamath, Audra McMillan, Adam Smith, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '19)
Distributed Differential Privacy via Shuffling
arXiv
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev
IACR International Conference on Theory and Application of Cryptographic Techniques (EUROCRYPT '19)
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev
IACR International Conference on Theory and Application of Cryptographic Techniques (EUROCRYPT '19)
The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy
Cynthia Dwork and Jonathan Ullman
Journal of Privacy and Confidentiality (JPC '18). Special issue commemorating Steve Feinberg.
Cynthia Dwork and Jonathan Ullman
Journal of Privacy and Confidentiality (JPC '18). Special issue commemorating Steve Feinberg.
Computing Marginals Using MapReduce
arXiv
Foto Afrati, Shantanu Sharma Jeffrey Ullman, and Jonathan Ullman
Journal of Computer and Systems Science (JCSS '18)
Foto Afrati, Shantanu Sharma Jeffrey Ullman, and Jonathan Ullman
Journal of Computer and Systems Science (JCSS '18)
Local Differential Privacy for Evolving Data
arXiv
Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner
Conference on Neural and Information Processing Systems (NeurIPS '18)
Selected as a Spotlight Presentation
Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner
Conference on Neural and Information Processing Systems (NeurIPS '18)
Selected as a Spotlight Presentation
The Limits of Post-Selection Generalization
arXiv
Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '18)
Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman
Conference on Neural and Information Processing Systems (NeurIPS '18)
Hardness of Non-Interactive Differential Privacy from One-Way Functions
ePrint
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Daniel Wichs
IACR International Cryptology Conference (CRYPTO '18)
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Daniel Wichs
IACR International Cryptology Conference (CRYPTO '18)
Skyline Identification in Multi-Armed Bandits
arXiv
Albert Cheu, Ravi Sundaram, and Jonathan Ullman
IEEE International Symposium on Information Theory (ISIT '18)
Albert Cheu, Ravi Sundaram, and Jonathan Ullman
IEEE International Symposium on Information Theory (ISIT '18)
An Antifolk Theorem for Large Repeated Games
arXiv
Mallesh Pai, Aaron Roth, and Jonathan Ullman
ACM Transactions on Economics and Computation (ACM TEAC '17)
Mallesh Pai, Aaron Roth, and Jonathan Ullman
ACM Transactions on Economics and Computation (ACM TEAC '17)
Between Pure and Approximate Differential Privacy
arXiv
Thomas Steinke and Jonathan Ullman
Journal of Privacy and Confidentiality (JPC '17)
Thomas Steinke and Jonathan Ullman
Journal of Privacy and Confidentiality (JPC '17)
Tight Bounds for Differentially Private Selection
arXiv
Thomas Steinke and Jonathan Ullman
IEEE Symposium on Foundations of Computer Science (FOCS '17)
Thomas Steinke and Jonathan Ullman
IEEE Symposium on Foundations of Computer Science (FOCS '17)
Fractional Set Cover in the Streaming Model
arXiv
Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan Ullman, Ali Vakilian, and Anak Yodpinyanee
International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX '17)
Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan Ullman, Ali Vakilian, and Anak Yodpinyanee
International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX '17)
The Price of Selection in Differential Privacy
arXiv
Mitali Bafna and Jonathan Ullman
Conference on Computational Learning Theory (COLT '17)
Mitali Bafna and Jonathan Ullman
Conference on Computational Learning Theory (COLT '17)
Multidimensional Dynamic Pricing for Welfare Maximization
arXiv
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, and Zhiwei Steven Wu
ACM Conference on Economics and Computation (EC '17)
Invited to a special issue of Transactions on Economics and Computation for EC '17
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, and Zhiwei Steven Wu
ACM Conference on Economics and Computation (EC '17)
Invited to a special issue of Transactions on Economics and Computation for EC '17
Make Up Your Mind: The Price of Online Queries in Differential Privacy
arXiv
Mark Bun, Thomas Steinke, Jonathan Ullman
ACM-SIAM Symposium on Discrete Algorithms (SODA '17)
Mark Bun, Thomas Steinke, Jonathan Ullman
ACM-SIAM Symposium on Discrete Algorithms (SODA '17)
Privacy Odometers and Filters: Pay-as-you-go Composition
arXiv
Ryan Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan
Conference on Neural and Information Processing Systems (NeurIPS '16)
Ryan Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan
Conference on Neural and Information Processing Systems (NeurIPS '16)
Strong Hardness of Privacy from Weak Traitor Tracing
arXiv
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Mark Zhandry
IACR Theory of Cryptography Conference (TCC '16B)
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Mark Zhandry
IACR Theory of Cryptography Conference (TCC '16B)
Space Lower Bounds for Itemset Frequency Sketches
arXiv
Edo Liberty, Michael Mitzenmacher, Justin Thaler, and Jonathan Ullman
ACM Symposium on Principles of Database Systems (PODS '16)
Edo Liberty, Michael Mitzenmacher, Justin Thaler, and Jonathan Ullman
ACM Symposium on Principles of Database Systems (PODS '16)
Algorithmic Stability for Adaptive Data Analysis
arXiv
Raef Bassily, Kobbi Nissim, Uri Stemmer, Adam Smith, Thomas Steinke, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '16)
Invited to a special issue of SIAM Journal on Computing for STOC '16
Raef Bassily, Kobbi Nissim, Uri Stemmer, Adam Smith, Thomas Steinke, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '16)
Invited to a special issue of SIAM Journal on Computing for STOC '16
Watch and Learn: Optimizing from Revealed Preferences Feedback
arXiv
SIGEcom Exchanges
Aaron Roth, Jonathan Ullman, and Zhiwei Steven Wu
ACM Symposium on Theory of Computing (STOC '16)
Aaron Roth, Jonathan Ullman, and Zhiwei Steven Wu
ACM Symposium on Theory of Computing (STOC '16)
When Can Limited Randomness Be Used in Repeated Games?
arXiv
Pavel Hubác̆ek, Moni Naor, Jonathan Ullman
International Symposium on Algorithmic Game Theory (SAGT '15)
Invited to a special issue of Theory of Computing for SAGT '15
Pavel Hubác̆ek, Moni Naor, Jonathan Ullman
International Symposium on Algorithmic Game Theory (SAGT '15)
Invited to a special issue of Theory of Computing for SAGT '15
Robust Traceability from Trace Amounts
PDF
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan
IEEE Symposium on Foundations of Computer Science (FOCS '15)
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan
IEEE Symposium on Foundations of Computer Science (FOCS '15)
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery
arXiv
Thomas Steinke and Jonathan Ullman
Conference on Computational Learning Theory (COLT '15)
Thomas Steinke and Jonathan Ullman
Conference on Computational Learning Theory (COLT '15)
Inducing Approximately Optimal Flow Using Truthful Mediators
arXiv
Ryan Rogers, Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu
ACM Conference on Economics and Computation (EC '15)
Ryan Rogers, Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu
ACM Conference on Economics and Computation (EC '15)
Private Multiplicative Weights Beyond Linear Queries
arXiv
Jonathan Ullman
ACM Symposium on Principles of Database Systems (PODS '15)
Jonathan Ullman
ACM Symposium on Principles of Database Systems (PODS '15)
Preventing False Discovery in Interactive Data Analysis is Hard
arXiv
Moritz Hardt and Jonathan Ullman
IEEE Symposium on Foundations of Computer Science (FOCS '14)
Moritz Hardt and Jonathan Ullman
IEEE Symposium on Foundations of Computer Science (FOCS '14)
Privately Solving Linear Programs
arXiv
Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman
International Colloquium on Automata, Languages, and Programming Track A (ICALP '14)
Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman
International Colloquium on Automata, Languages, and Programming Track A (ICALP '14)
Fingerprinting Codes and the Price of Approximate Differential Privacy
arXiv
Mark Bun, Jonathan Ullman, and Salil Vadhan
ACM Symposium on Theory of Computing (STOC '14)
Invited to a special issue of SIAM Journal of Computing for STOC '14
Mark Bun, Jonathan Ullman, and Salil Vadhan
ACM Symposium on Theory of Computing (STOC '14)
Invited to a special issue of SIAM Journal of Computing for STOC '14
Faster Private Release of Marginals on Small Databases
arXiv
Karthekeyan Chandrasekaran, Justin Thaler, Jonathan Ullman, and Andrew Wan
Innovations in Theoretical Computer Science (ITCS '14)
Karthekeyan Chandrasekaran, Justin Thaler, Jonathan Ullman, and Andrew Wan
Innovations in Theoretical Computer Science (ITCS '14)
Robust Mediators in Large Games
arXiv
Michael Kearns, Mallesh Pai, Ryan Rogers, Aaron Roth, and Jonathan Ullman
Innovations in Theoretical Computer Science (ITCS '14)
Michael Kearns, Mallesh Pai, Ryan Rogers, Aaron Roth, and Jonathan Ullman
Innovations in Theoretical Computer Science (ITCS '14)
Differential Privacy for the Analyst via Private Equilibrium Computation
arXiv
Justin Hsu, Aaron Roth, and Jonathan Ullman
ACM Symposium on Thoery of Computing (STOC '13)
Justin Hsu, Aaron Roth, and Jonathan Ullman
ACM Symposium on Thoery of Computing (STOC '13)
Answering n2+o(1) Counting Queries with Differential Privacy is Hard
arXiv
Jonathan Ullman
ACM Symposium on Thoery of Computing (STOC '13)
Invited to a special issue of SIAM Journal of Computing for STOC '13
Jonathan Ullman
ACM Symposium on Thoery of Computing (STOC '13)
Invited to a special issue of SIAM Journal of Computing for STOC '13
Faster Algorithms for Privately Releasing Marginals
arXiv
Justin Thaler, Jonathan Ullman, and Salil Vadhan
International Colloquium on Automata, Languages, and Programming Track A (ICALP '12)
Justin Thaler, Jonathan Ullman, and Salil Vadhan
International Colloquium on Automata, Languages, and Programming Track A (ICALP '12)
Iterative Constructions and Private Data Release
arXiv
Anupam Gupta, Aaron Roth, and Jonathan Ullman
IACR Theory of Cryptography Conference (TCC '12)
Anupam Gupta, Aaron Roth, and Jonathan Ullman
IACR Theory of Cryptography Conference (TCC '12)
Privately Releasing Conjunctions and the Statistical Query Barrier
arXiv
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '12)
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '12)
PCPs and the Hardness of Generating Private Synthetic Data
ECCC
Jonathan Ullman and Salil Vadhan
IACR Theory of Cryptography Conference (TCC '11)
Invited to the Journal of Cryptology
Jonathan Ullman and Salil Vadhan
IACR Theory of Cryptography Conference (TCC '11)
Invited to the Journal of Cryptology
Course Allocation by Proxy Auction
Scott Kominers, Mike Ruberry, Jonathan Ullman
Workshop on Internet and Network Economics (WINE '10)
Scott Kominers, Mike Ruberry, Jonathan Ullman
Workshop on Internet and Network Economics (WINE '10)
The Price of Privately Releasing Contingency Tables and the Spectra of Random Matrices with Correlated Rows
Shiva Kasiviswanathan, Mark Rudelson, Adam Smith, Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '10)
Shiva Kasiviswanathan, Mark Rudelson, Adam Smith, Jonathan Ullman
ACM Symposium on Theory of Computing (STOC '10)
Secondary Publications
Some of my work appears in journals as a secondary form of publication, after initially appearing in a computer science conference. This list includes all such publications.
Manipulation Attacks in Local Differential Privacy
arXiv 1m Video
Albert Cheu, Adam Smith, and Jonathan Ullman
Journal of Privacy and Confidentiality, 2021
Albert Cheu, Adam Smith, and Jonathan Ullman
Journal of Privacy and Confidentiality, 2021
Efficiently Estimating Erdős-Rényi Graphs with Differential Privacy
arXiv
Poster
Adam Sealfon and Jonathan Ullman
Journal of Privacy and Confidentiality, 2021
Adam Sealfon and Jonathan Ullman
Journal of Privacy and Confidentiality, 2021
PCPs and the Hardness of Generating Private Synthetic Data
ECCC
Jonathan Ullman and Salil Vadhan Journal of Cryptology, 2020
Invited submission from TCC '11
Jonathan Ullman and Salil Vadhan Journal of Cryptology, 2020
Invited submission from TCC '11
Multidimensional Dynamic Pricing for Welfare Maximization
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, and Zhiwei Steven Wu
ACM Transactions on Economics and Computation, 2020
Special issue for invited papers from EC '17
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, and Zhiwei Steven Wu
ACM Transactions on Economics and Computation, 2020
Special issue for invited papers from EC '17
Fingerprinting Codes and the Price of Approximate Differential Privacy
arXiv
Mark Bun, Jonathan Ullman, and Salil Vadhan
SIAM Journal on Computing, 2018
Special issue for invited papers from STOC '14
Mark Bun, Jonathan Ullman, and Salil Vadhan
SIAM Journal on Computing, 2018
Special issue for invited papers from STOC '14
When Can Limited Randomness Be Used in Repeated Games?
arXiv
Pavel Hubác̆ek, Moni Naor, and Jonathan Ullman
Theory of Computing Systems, 2016
Special issue for invited papers from SAGT '15
Pavel Hubác̆ek, Moni Naor, and Jonathan Ullman
Theory of Computing Systems, 2016
Special issue for invited papers from SAGT '15
Answering n2+o(1) Counting Queries with Differential Privacy is Hard
arXiv
Jonathan Ullman
SIAM Journal on Computing, 2016
Special issue for invited papers from STOC '13
Jonathan Ullman
SIAM Journal on Computing, 2016
Special issue for invited papers from STOC '13
Privately Releasing Conjunctions and the Statistical Query Barrier
arXiv
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman
SIAM Journal on Computing, 2013
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman
SIAM Journal on Computing, 2013
Surveys and Other Writing
DifferentialPrivacy.org
Website
Blog
Subgaussian Concentration via Stability Arguments
arXiv
Thomas Steinke and
Jonathan Ullman
Technical Perspective: Building a Safety Net for Data Reuse
CACM
Jonathan Ullman
Communications of the ACM, 2017
PSI (Ψ): A Private data Sharing Interface
arXiv
Marco Gaboardi, James Honaker, Gary King, Jack Murtagh, Kobbi Nissim, Jonathan Ullman, and Salil Vadhan
Marco Gaboardi, James Honaker, Gary King, Jack Murtagh, Kobbi Nissim, Jonathan Ullman, and Salil Vadhan
Exposed! A Survey of Attacks on Private Data
ARSIA
Cynthia Dwork,
Adam Smith,
Thomas Steinke, and
Jonathan Ullman
Annual Review of Statistics and its Applications