Jonathan Ullman
Jonathan Ullman
Assistant Professor of Computer Science, Northeastern University
Bestätigte E-Mail-Adresse bei ccs.neu.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Algorithmic stability for adaptive data analysis
R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
SIAM Journal on Computing, STOC16-377-STOC16-405, 2021
2042021
Iterative constructions and private data release
A Gupta, A Roth, J Ullman
Theory of cryptography conference, 339-356, 2012
1712012
Privately releasing conjunctions and the statistical query barrier
A Gupta, M Hardt, A Roth, J Ullman
SIAM Journal on Computing 42 (4), 1494-1520, 2013
1402013
Fingerprinting codes and the price of approximate differential privacy
M Bun, J Ullman, S Vadhan
SIAM Journal on Computing 47 (5), 1888-1938, 2018
1282018
Robust mediators in large games
M Kearns, MM Pai, R Rogers, A Roth, J Ullman
arXiv preprint arXiv:1512.02698, 2015
119*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
1082015
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
1062017
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
1052010
Distributed Differential Privacy via Shuffling
A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev
102*2018
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
952014
Answering Counting Queries with Differential Privacy is Hard
J Ullman
SIAM Journal on Computing 45 (2), 473-496, 2016
902016
PCPs and the hardness of generating private synthetic data
J Ullman, S Vadhan
Theory of Cryptography Conference, 400-416, 2011
88*2011
Between pure and approximate differential privacy
T Steinke, J Ullman
arXiv preprint arXiv:1501.06095, 2015
842015
Interactive fingerprinting codes and the hardness of preventing false discovery
T Steinke, J Ullman
Conference on learning theory, 1588-1628, 2015
832015
Faster algorithms for privately releasing marginals
J Thaler, J Ullman, S Vadhan
International Colloquium on Automata, Languages, and Programming, 810-821, 2012
802012
PSI: a Private data Sharing Interface
M Gaboardi, J Honaker, G King, K Nissim, J Ullman, S Vadhan
arXiv preprint arXiv:1609.04340, 2016
532016
Privacy odometers and filters: Pay-as-you-go composition
R Rogers, A Roth, J Ullman, S Vadhan
arXiv preprint arXiv:1605.08294, 2016
512016
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
492019
Private multiplicative weights beyond linear queries
J Ullman
Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2015
472015
Privately solving linear programs
J Hsu, A Roth, T Roughgarden, J Ullman
International Colloquium on Automata, Languages, and Programming, 612-624, 2014
452014
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