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Jalaj Upadhyay
Jalaj Upadhyay
Assistant Professor, Rutgers
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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
1792017
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
632020
Block-wise non-malleable codes
N Chandran, V Goyal, P Mukherjee, O Pandey, J Upadhyay
Cryptology ePrint Archive, 2015
562015
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
562012
On differentially private graph sparsification and applications
R Arora, J Upadhyay
Advances in neural information processing systems 32, 2019
482019
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
292023
The price of privacy for low-rank factorization
J Upadhyay
Advances in Neural Information Processing Systems 31, 2018
292018
Random projections, graph sparsification, and differential privacy
J Upadhyay
International Conference on the Theory and Application of Cryptology and …, 2013
282013
Randomness efficient fast-johnson-lindenstrauss transform with applications in differential privacy and compressed sensing
J Upadhyay
arXiv preprint arXiv:1410.2470, 2014
232014
Differentially private linear algebra in the streaming model
J Upadhyay
arXiv preprint arXiv:1409.5414, 2014
232014
Sublinear space private algorithms under the sliding window model
J Upadhyay
International Conference on Machine Learning, 6363-6372, 2019
222019
Fast and space-optimal low-rank factorization in the streaming model with application in differential privacy
J Upadhyay
arXiv preprint arXiv:1604.01429, 2016
212016
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
18*2023
Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization
A Ganesh, A Thakurta, J Upadhyay
arXiv preprint arXiv:2204.01585, 2022
182022
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
182013
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
17*2023
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
162021
Multi-prover proof of retrievability
MB Paterson, DR Stinson, J Upadhyay
Journal of Mathematical Cryptology 12 (4), 203-220, 2018
162018
Differentially private robust low-rank approximation
R Arora, J Upadhyay
Advances in neural information processing systems 31, 2018
152018
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