Jonathan Ullman

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Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Thomas SteinkeGoogle DeepMindVerified email at google.com
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard UniversityVerified email at g.harvard.edu
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector InstituteVerified email at uwaterloo.ca
Alina OpreaNortheastern UniversityVerified email at neu.edu
Albert CheuVisiting Researcher, GoogleVerified email at google.com
Matthew JagielskiGoogle DeepMindVerified email at google.com
Kobbi NissimDept. of Computer Science (Georgetown)Verified email at georgetown.edu
Vikrant SinghalResearch Associate, Harvard UniversityVerified email at seas.harvard.edu
Cynthia DworkMicrosoft ResearchVerified email at microsoft.com
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Mallesh M. PaiRice UniversityVerified email at rice.edu
Lydia ZakynthinouPostdoctoral Researcher, UC BerkeleyVerified email at northeastern.edu
Raef BassilyAssociate Professor, Computer Science & Engineering, The Ohio State UniversityVerified email at osu.edu
Justin ThalerGeorgetown UniversityVerified email at georgetown.edu
Anupam GuptaProfessor of Computer Science at New York UniversityVerified email at nyu.edu
Uri StemmerTel Aviv UniversityVerified email at tau.ac.il
Moritz HardtMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de

Jonathan Ullman

Associate Professor of Computer Science, Northeastern University

Verified email at ccs.neu.edu - Homepage

Differential Privacy Machine Learning Theory Cryptography


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Distributed Differential Privacy via Shuffling A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev	410	2018
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	314	2017
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	310	2016
Fingerprinting codes and the price of approximate differential privacy M Bun, J Ullman, S Vadhan SIAM Journal on Computing 47 (5), 1888-1938, 2018	240*	2018
Iterative constructions and private data release A Gupta, A Roth, J Ullman Theory of Cryptography: 9th Theory of Cryptography Conference, TCC 2012 …, 2012	229	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	221	2020
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	207	2015
Robust mediators in large games M Kearns, MM Pai, R Rogers, A Roth, J Ullman arXiv preprint arXiv:1512.02698, 2015	204*	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	177	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	166	2011
Between pure and approximate differential privacy T Steinke, J Ullman arXiv preprint arXiv:1501.06095, 2015	164	2015
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	159	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	137	2014
PCPs and the hardness of generating private synthetic data J Ullman, S Vadhan Theory of Cryptography Conference, 400-416, 2011	134*	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	130	2010
Interactive fingerprinting codes and the hardness of preventing false discovery T Steinke, J Ullman Conference on learning theory, 1588-1628, 2015	122	2015
Answering n^{2+o(1)} counting queries with differential privacy is hard J Ullman SIAM Journal on Computing 45 (2), 473-496, 2016	116	2016
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	109	2020
Faster algorithms for privately releasing marginals J Thaler, J Ullman, S Vadhan International Colloquium on Automata, Languages, and Programming, 810-821, 2012	106	2012
Local differential privacy for evolving data M Joseph, A Roth, J Ullman, B Waggoner Advances in Neural Information Processing Systems 31, 2018	105	2018

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