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
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
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
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
Differentially private online learning
P Jain, P Kothari, A Thakurta
Conference on Learning Theory, 24.1-24.34, 2012
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
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Thakurta
arXiv preprint arXiv:1707.04982, 2017
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
Nearly optimal private lasso
K Talwar, A Guha Thakurta, L Zhang
Advances in Neural Information Processing Systems 28, 3025-3033, 2015
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
International Conference on the Theory and Application of Cryptology and …, 2011
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
Privacy amplification by iteration
V Feldman, I Mironov, K Talwar, A Thakurta
2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018
Private stochastic convex optimization with optimal rates
R Bassily, V Feldman, K Talwar, A Thakurta
arXiv preprint arXiv:1908.09970, 2019
Learning new words
AG Thakurta, AH Vyrros, US Vaishampayan, G Kapoor, J Freudiger, ...
US Patent 9,594,741, 2017
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
Private empirical risk minimization, revisited
R Bassily, A Smith, A Thakurta
rem 3, 19, 2014
Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry
K Talwar, A Thakurta, L Zhang
arXiv preprint arXiv:1411.5417, 2014
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