Jalaj Upadhyay
Jalaj Upadhyay
Assistant Professor, Rutgers
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Citat de
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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
Block-wise non-malleable codes
N Chandran, V Goyal, P Mukherjee, O Pandey, J Upadhyay
Cryptology ePrint Archive, 2015
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
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
On differentially private graph sparsification and applications
R Arora, J Upadhyay
Advances in neural information processing systems 32, 2019
Random projections, graph sparsification, and differential privacy
J Upadhyay
International Conference on the Theory and Application of Cryptology and …, 2013
The price of privacy for low-rank factorization
J Upadhyay
Advances in Neural Information Processing Systems 31, 2018
Randomness efficient fast-johnson-lindenstrauss transform with applications in differential privacy and compressed sensing
J Upadhyay
arXiv preprint arXiv:1410.2470, 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
Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation
H Fichtenberger, M Henzinger, J Upadhyay
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
Sublinear space private algorithms under the sliding window model
J Upadhyay
International Conference on Machine Learning, 6363-6372, 2019
Differentially private linear algebra in the streaming model
J Upadhyay
arXiv preprint arXiv:1409.5414, 2014
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets
A Ganesh, A Thakurta, J Upadhyay
The Thirty Sixth Annual Conference on Learning Theory, 1730-1773, 2023
A framework for private matrix analysis in sliding window model
J Upadhyay, S Upadhyay
International Conference on Machine Learning, 10465-10475, 2021
Multi-prover proof of retrievability
MB Paterson, DR Stinson, J Upadhyay
Journal of Mathematical Cryptology 12 (4), 203-220, 2018
Differentially private robust low-rank approximation
R Arora, J Upadhyay
Advances in neural information processing systems 31, 2018
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
Differentially private analysis on graph streams
J Upadhyay, S Upadhyay, R Arora
International Conference on Artificial Intelligence and Statistics, 1171-1179, 2021
Constant matters: Fine-grained complexity of differentially private continual observation, 2022
H Fichtenberger, M Henzinger, J Upadhyay
URL https://arxiv. org/abs/2202.11205, 0
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