Abhradeep Guha Thakurta
Cited by
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R Bassily, A Smith, A Thakurta
2014 IEEE 55th annual symposium on foundations of computer science, 464-473, 2014
Amplification by shuffling: From local to central differential privacy via anonymity
Ú Erlingsson, V Feldman, I Mironov, A Raghunathan, K Talwar, ...
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019
Private convex empirical risk minimization and high-dimensional regression
D Kifer, A Smith, A Thakurta
Conference on Learning Theory, 25.1-25.40, 2012
GUPT: privacy preserving data analysis made easy
P Mohan, A Thakurta, E Shi, D Song, D Culler
Proceedings of the 2012 ACM SIGMOD International Conference on Management of …, 2012
Analyze gauss: optimal bounds for privacy-preserving principal component analysis
C Dwork, K Talwar, A Thakurta, L Zhang
Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014
Discovering frequent patterns in sensitive data
R Bhaskar, S Laxman, A Smith, A Thakurta
Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Guha Thakurta
Advances in Neural Information Processing Systems 30, 2017
Differentially private online learning
P Jain, P Kothari, A Thakurta
Conference on Learning Theory, 24.1-24.34, 2012
Private stochastic convex optimization with optimal rates
R Bassily, V Feldman, K Talwar, A Guha Thakurta
Advances in neural information processing systems 32, 2019
Adversary instantiation: Lower bounds for differentially private machine learning
M Nasr, S Songi, A Thakurta, N Papernot, N Carlin
2021 IEEE Symposium on security and privacy (SP), 866-882, 2021
Towards practical differentially private convex optimization
R Iyengar, JP Near, D Song, O Thakkar, A Thakurta, L Wang
2019 IEEE symposium on security and privacy (SP), 299-316, 2019
Differentially private feature selection via stability arguments, and the robustness of the lasso
AG Thakurta, A Smith
Conference on Learning Theory, 819-850, 2013
Is interaction necessary for distributed private learning?
A Smith, A Thakurta, J Upadhyay
2017 IEEE Symposium on Security and Privacy (SP), 58-77, 2017
Privacy amplification by iteration
V Feldman, I Mironov, K Talwar, A Thakurta
2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018
Nearly optimal private lasso
K Talwar, A Guha Thakurta, L Zhang
Advances in Neural Information Processing Systems 28, 2015
Tempered sigmoid activations for deep learning with differential privacy
N Papernot, A Thakurta, S Song, S Chien, Ú Erlingsson
Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 9312-9321, 2021
Practical and private (deep) learning without sampling or shuffling
P Kairouz, B McMahan, S Song, O Thakkar, A Thakurta, Z Xu
International Conference on Machine Learning, 5213-5225, 2021
Differentially private learning with kernels
P Jain, A Thakurta
International conference on machine learning, 118-126, 2013
Noiseless database privacy
R Bhaskar, A Bhowmick, V Goyal, S Laxman, A Thakurta
Advances in Cryptology–ASIACRYPT 2011: 17th International Conference on the …, 2011
Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Ú Erlingsson, V Feldman, I Mironov, A Raghunathan, S Song, K Talwar, ...
arXiv preprint arXiv:2001.03618, 2020
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