| 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 | 638 | 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 | 323 | 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 | 291 | 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 | 281 | 2010 |
| 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 | 241 | 2019 |
| 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 | 237 | 2014 |
| Differentially private online learning P Jain, P Kothari, A Thakurta Conference on Learning Theory, 24.1-24.34, 2012 | 212 | 2012 |
| Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Guha Thakurta Advances in Neural Information Processing Systems 30, 2017 | 203 | 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 | 148 | 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 | 127 | 2017 |
| Nearly optimal private lasso K Talwar, A Guha Thakurta, L Zhang Advances in Neural Information Processing Systems 28, 2015 | 127 | 2015 |
| Private stochastic convex optimization with optimal rates R Bassily, V Feldman, K Talwar, A Guha Thakurta Advances in neural information processing systems 32, 2019 | 116 | 2019 |
| 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 | 101 | 2019 |
| Differentially private learning with kernels P Jain, A Thakurta International conference on machine learning, 118-126, 2013 | 101 | 2013 |
| Privacy amplification by iteration V Feldman, I Mironov, K Talwar, A Thakurta 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 96 | 2018 |
| Noiseless database privacy R Bhaskar, A Bhowmick, V Goyal, S Laxman, A Thakurta International Conference on the Theory and Application of Cryptology and …, 2011 | 93 | 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 | 67 | 2020 |
| 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 | 60 | 2021 |
| Learning new words AG Thakurta, AH Vyrros, US Vaishampayan, G Kapoor, J Freudiger, ... US Patent 9,594,741, 2017 | 51 | 2017 |
| Adversary instantiation: Lower bounds for differentially private machine learning M Nasr, S Songi, A Thakurta, N Papemoti, N Carlin 2021 IEEE Symposium on Security and Privacy (SP), 866-882, 2021 | 48 | 2021 |
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
