Jalaj Upadhyay

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Abhradeep Guha ThakurtaGoogle Research - Brain TeamVerified email at google.com
Monika HenzingerProfessor of Computer Science, Institute of Science and Technology Austria (ISTA)Verified email at ist.ac.at
Douglas StinsonProfessor Emeritus, David R. Cheriton School of Computer Science, University of WaterlooVerified email at uwaterloo.ca
Sarvagya UpadhyayFujitsu Research of AmericaVerified email at fujitsu.com
Raman AroraDepartment of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Arun GaneshResearch Scientist, GoogleVerified email at google.com
Samson ZhouTexas A&M UniversityVerified email at alum.mit.edu
Vladimir BravermanVictor E. Cameron Professor of Computer Science, Rice UniversityVerified email at rice.edu
Petros DrineasProfessor, Computer Science Department, Purdue UniversityVerified email at purdue.edu
Paterson MBProfessor of Mathematics, Birkbeck, University of LondonVerified email at bbk.ac.uk
David WoodruffProfessor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstVerified email at cs.umass.edu
Christopher MuscoAssistant Professor, New York UniversityVerified email at nyu.edu
Pratyay MukherjeeSupra ResearchVerified email at supraoracles.com
Vipul GoyalNTT ResearchVerified email at vipulgoyal.org
Omkant PandeyStony Brook UniversityVerified email at cs.stonybrook.edu
Nishanth ChandranPrincipal Researcher, Microsoft Research, IndiaVerified email at microsoft.com
Simon R. BlackburnRoyal Holloway University of LondonVerified email at rhul.ac.uk
Jie GaoProfessor of Computer Science, Rutgers UniversityVerified email at cs.rutgers.edu

Jalaj Upadhyay

Assistant Professor, Rutgers

Verified email at apple.com - Homepage

Differential Privacy Theoretical Computer Science


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Is interaction necessary for distributed private learning? A Smith, A Thakurta, J Upadhyay 2017 IEEE Symposium on Security and Privacy (SP), 58-77, 2017	182	2017
Near optimal linear algebra in the online and sliding window models V Braverman, P Drineas, C Musco, C Musco, J Upadhyay, DP Woodruff, ... 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS …, 2020	65	2020
Block-wise non-malleable codes N Chandran, V Goyal, P Mukherjee, O Pandey, J Upadhyay Cryptology ePrint Archive, 2015	56	2015
On the complexity of the herding attack and some related attacks on hash functions SR Blackburn, DR Stinson, J Upadhyay Designs, Codes and Cryptography 64, 171-193, 2012	56	2012
On differentially private graph sparsification and applications R Arora, J Upadhyay Advances in neural information processing systems 32, 2019	52	2019
Almost tight error bounds on differentially private continual counting M Henzinger, J Upadhyay, S Upadhyay Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023	32	2023
The price of privacy for low-rank factorization J Upadhyay Advances in Neural Information Processing Systems 31, 2018	30	2018
Random projections, graph sparsification, and differential privacy J Upadhyay International Conference on the Theory and Application of Cryptology and …, 2013	30	2013
Sublinear space private algorithms under the sliding window model J Upadhyay International Conference on Machine Learning, 6363-6372, 2019	26	2019
Randomness efficient fast-johnson-lindenstrauss transform with applications in differential privacy and compressed sensing J Upadhyay arXiv preprint arXiv:1410.2470, 2014	23	2014
Differentially private linear algebra in the streaming model J Upadhyay arXiv preprint arXiv:1409.5414, 2014	23	2014
Fast and space-optimal low-rank factorization in the streaming model with application in differential privacy J Upadhyay arXiv preprint arXiv:1604.01429, 2016	22	2016
Constant matters: Fine-grained error bound on differentially private continual observation H Fichtenberger, M Henzinger, J Upadhyay International Conference on Machine Learning, 10072-10092, 2023	20*	2023
Constant matters: Fine-grained complexity of differentially private continual observation using completely bounded norms M Henzinger, J Upadhyay Cryptology ePrint Archive, 2022	20	2022
Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization A Ganesh, A Thakurta, J Upadhyay arXiv preprint arXiv:2204.01585, 2022	19	2022
A coding theory foundation for the analysis of general unconditionally secure proof-of-retrievability schemes for cloud storage MB Paterson, DR Stinson, J Upadhyay Journal of Mathematical Cryptology 7 (3), 183-216, 2013	18	2013
A framework for private matrix analysis in sliding window model J Upadhyay, S Upadhyay International Conference on Machine Learning, 10465-10475, 2021	17*	2021
Differentially private analysis on graph streams J Upadhyay, S Upadhyay, R Arora International Conference on Artificial Intelligence and Statistics, 1171-1179, 2021	17	2021
Multi-prover proof of retrievability MB Paterson, DR Stinson, J Upadhyay Journal of Mathematical Cryptology 12 (4), 203-220, 2018	17	2018
Differentially private robust low-rank approximation R Arora, J Upadhyay Advances in neural information processing systems 31, 2018	16	2018

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