| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 2019 | 237 | 2019 |
| The composition theorem for differential privacy P Kairouz, S Oh, P Viswanath IEEE Transactions on Information Theory 63 (6), 4037-4049, 2017 | 217 | 2017 |
| Extremal mechanisms for local differential privacy P Kairouz, S Oh, P Viswanath Advances in neural information processing systems, 2879-2887, 2014 | 189 | 2014 |
| Discrete distribution estimation under local privacy P Kairouz, K Bonawitz, D Ramage arXiv preprint arXiv:1602.07387, 2016 | 120 | 2016 |
| The staircase mechanism in differential privacy Q Geng, P Kairouz, S Oh, P Viswanath IEEE Journal of Selected Topics in Signal Processing 9 (7), 1176-1184, 2015 | 77 | 2015 |
| Context-aware generative adversarial privacy C Huang, P Kairouz, X Chen, L Sankar, R Rajagopal Entropy 19 (12), 656, 2017 | 76 | 2017 |
| Extremal mechanisms for local differential privacy P Kairouz, S Oh, P Viswanath The Journal of Machine Learning Research 17 (1), 492-542, 2016 | 73 | 2016 |
| Spy vs. spy: Rumor source obfuscation G Fanti, P Kairouz, S Oh, P Viswanath Proceedings of the 2015 ACM SIGMETRICS International Conference on …, 2015 | 54 | 2015 |
| Secure multi-party differential privacy P Kairouz, S Oh, P Viswanath Advances in neural information processing systems, 2008-2016, 2015 | 30 | 2015 |
| Hiding the rumor source G Fanti, P Kairouz, S Oh, K Ramchandran, P Viswanath IEEE Transactions on Information Theory 63 (10), 6679-6713, 2017 | 29 | 2017 |
| A general approach to adding differential privacy to iterative training procedures HB McMahan, G Andrew, U Erlingsson, S Chien, I Mironov, N Papernot, ... arXiv preprint arXiv:1812.06210, 2018 | 28 | 2018 |
| The composition theorem for differential privacy S Oh, P Viswanath arXiv preprint arXiv:1311.0776, 2013 | 27 | 2013 |
| Generative adversarial privacy C Huang, P Kairouz, X Chen, L Sankar, R Rajagopal arXiv preprint arXiv:1807.05306, 2018 | 26 | 2018 |
| Can you really backdoor federated learning? Z Sun, P Kairouz, AT Suresh, HB McMahan arXiv preprint arXiv:1911.07963, 2019 | 22 | 2019 |
| Generative models for effective ml on private, decentralized datasets S Augenstein, HB McMahan, D Ramage, S Ramaswamy, P Kairouz, ... arXiv preprint arXiv:1911.06679, 2019 | 21 | 2019 |
| Rumor source obfuscation on irregular trees G Fanti, P Kairouz, S Oh, K Ramchandran, P Viswanath ACM SIGMETRICS Performance Evaluation Review 44 (1), 153-164, 2016 | 21 | 2016 |
| Differentially private multi-party computation: Optimality of non-interactive randomized response P Kairouz, S Oh, P Viswanath arXiv preprint arXiv:1407.1546, 2014 | 14 | 2014 |
| Variable block length and superposition coding for hybrid automatic repeat request P Kairouz, AK Sadek, TA Kadous US Patent App. 14/025,713, 2014 | 14 | 2014 |
| Dp-cgan: Differentially private synthetic data and label generation R Torkzadehmahani, P Kairouz, B Paten Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 13 | 2019 |
| Federated heavy hitters discovery with differential privacy W Zhu, P Kairouz, B McMahan, H Sun, W Li International Conference on Artificial Intelligence and Statistics, 3837-3847, 2020 | 11 | 2020 |
Pramod ViswanathGilmore Family Endowed Professor of Electrical and Computer Engineering, University of Illinois atVerified email at illinois.edu
