Jonathan Ullman
Jonathan Ullman
Associate Professor of Computer Science, Northeastern University
Zweryfikowany adres z ccs.neu.edu - Strona główna
Cytowane przez
Cytowane przez
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
Distributed Differential Privacy via Shuffling
A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev
Iterative constructions and private data release
A Gupta, A Roth, J Ullman
Theory of cryptography conference, 339-356, 2012
Exposed! a survey of attacks on private data
C Dwork, A Smith, T Steinke, J Ullman
Annu. Rev. Stat. Appl 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
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
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
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
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
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
PCPs and the hardness of generating private synthetic data
J Ullman, S Vadhan
Theory of Cryptography Conference, 400-416, 2011
Answering n^{2+o(1)} counting queries with differential privacy is hard
J Ullman
SIAM Journal on Computing 45 (2), 473-496, 2016
Interactive fingerprinting codes and the hardness of preventing false discovery
T Steinke, J Ullman
Conference on learning theory, 1588-1628, 2015
Faster algorithms for privately releasing marginals
J Thaler, J Ullman, S Vadhan
International Colloquium on Automata, Languages, and Programming, 810-821, 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
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
PSI: a Private data Sharing Interface
M Gaboardi, J Honaker, G King, K Nissim, J Ullman, S Vadhan
arXiv preprint arXiv:1609.04340, 2016
Privacy odometers and filters: Pay-as-you-go composition
RM Rogers, A Roth, J Ullman, S Vadhan
Advances in Neural Information Processing Systems 29, 2016
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