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 (1), 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
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
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
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
PCPs and the hardness of generating private synthetic data
J Ullman, S Vadhan
Theory of Cryptography Conference, 400-416, 2011
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
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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