| 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 | 1034 | 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 | 451 | 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 | 446 | 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 | 337 | 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 | 326 | 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 | 314 | 2010 |
| Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Guha Thakurta Advances in Neural Information Processing Systems 30, 2017 | 281 | 2017 |
| Differentially private online learning P Jain, P Kothari, A Thakurta Conference on Learning Theory, 24.1-24.34, 2012 | 269 | 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 | 235 | 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 | 198 | 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 | 197 | 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 | 197 | 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 | 179 | 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 | 176 | 2018 |
| Nearly optimal private lasso K Talwar, A Guha Thakurta, L Zhang Advances in Neural Information Processing Systems 28, 2015 | 176 | 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 | 164 | 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 | 153 | 2021 |
| Differentially private learning with kernels P Jain, A Thakurta International conference on machine learning, 118-126, 2013 | 119 | 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 | 116 | 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 | 96 | 2020 |
Adam SmithProfessor of Computer Science, Boston University在 bu.edu 的电子邮件经过验证
