Jonathan Ullman
Jonathan Ullman
Associate Professor of Computer Science, Northeastern University
Verified email at - Homepage
Cited by
Cited by
Distributed Differential Privacy via Shuffling
A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev
Algorithmic stability for adaptive data analysis
R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
Exposed! a survey of attacks on private data
C Dwork, A Smith, T Steinke, J Ullman
Annual Review of Statistics and Its Application 4, 61-84, 2017
Fingerprinting codes and the price of approximate differential privacy
M Bun, J Ullman, S Vadhan
SIAM Journal on Computing 47 (5), 1888-1938, 2018
Iterative constructions and private data release
A Gupta, A Roth, J Ullman
Theory of Cryptography: 9th Theory of Cryptography Conference, TCC 2012 …, 2012
Robust mediators in large games
M Kearns, MM Pai, R Rogers, A Roth, J Ullman
arXiv preprint arXiv:1512.02698, 2015
Robust traceability from trace amounts
C Dwork, A Smith, T Steinke, J Ullman, S Vadhan
2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 650-669, 2015
Auditing differentially private machine learning: How private is private sgd?
M Jagielski, J Ullman, A Oprea
Advances in Neural Information Processing Systems 33, 22205-22216, 2020
Differentially private fair learning
M Jagielski, M Kearns, J Mao, A Oprea, A Roth, S Sharifi-Malvajerdi, ...
International Conference on Machine Learning, 3000-3008, 2019
Privately releasing conjunctions and the statistical query barrier
A Gupta, M Hardt, A Roth, J Ullman
Proceedings of the forty-third annual ACM symposium on Theory of computing …, 2011
Between pure and approximate differential privacy
T Steinke, J Ullman
arXiv preprint arXiv:1501.06095, 2015
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
Preventing false discovery in interactive data analysis is hard
M Hardt, J Ullman
Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on …, 2014
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
SP Kasiviswanathan, M Rudelson, A Smith, J Ullman
Proceedings of the forty-second ACM symposium on Theory of computing, 775-784, 2010
PCPs and the hardness of generating private synthetic data
J Ullman, S Vadhan
Theory of Cryptography Conference, 400-416, 2011
Interactive fingerprinting codes and the hardness of preventing false discovery
T Steinke, J Ullman
Conference on learning theory, 1588-1628, 2015
Answering n^{2+o(1)} counting queries with differential privacy is hard
J Ullman
SIAM Journal on Computing 45 (2), 473-496, 2016
Faster algorithms for privately releasing marginals
J Thaler, J Ullman, S Vadhan
International Colloquium on Automata, Languages, and Programming, 810-821, 2012
Local differential privacy for evolving data
M Joseph, A Roth, J Ullman, B Waggoner
Advances in Neural Information Processing Systems 31, 2018
Coinpress: Practical private mean and covariance estimation
S Biswas, Y Dong, G Kamath, J Ullman
Advances in Neural Information Processing Systems 33, 14475-14485, 2020
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