| Privacy at scale: Local differential privacy in practice G Cormode, S Jha, T Kulkarni, N Li, D Srivastava, T Wang Proceedings of the 2018 International Conference on Management of Data, 1655 …, 2018 | 280 | 2018 |
| Marginal release under local differential privacy G Cormode, T Kulkarni, D Srivastava Proceedings of the 2018 International Conference on Management of Data, 131-146, 2018 | 141 | 2018 |
| Answering range queries under local differential privacy T Kulkarni Proceedings of the 2019 International Conference on Management of Data, 1832 …, 2019 | 111 | 2019 |
| Differentially Private Bayesian Inference for Generalized Linear Models T Kulkarni, J Jälkö, A Koskela, S Kaski, A Honkela arXiv preprint arXiv:2011.00467, 2020 | 29 | 2020 |
| Constrained private mechanisms for count data G Cormode, T Kulkarni, D Srivastava IEEE Transactions on Knowledge and Data Engineering 33 (2), 415-430, 2019 | 16* | 2019 |
| Robust leader election in a fast-changing world J Augustine, T Kulkarni, P Nakhe, P Robinson arXiv preprint arXiv:1310.4908, 2013 | 11 | 2013 |
| Leader Election in Sparse Dynamic Networks with Churn SS John Augustine, Tejas Kulkarni 2015 IEEE International Parallel and Distributed Processing Symposium, IPDPS …, 2015 | 10* | 2015 |
| Private protocols for U-statistics in the local model and beyond J Bell, A Bellet, A Gascón, T Kulkarni International Conference on Artificial Intelligence and Statistics, 1573-1583, 2020 | 7 | 2020 |
| Practical Differentially Private Hyperparameter Tuning with Subsampling A Koskela, K Tejas https://arxiv.org/pdf/2301.11989.pdf, 2023 | 3 | 2023 |
| Locally Differentially Private Bayesian Inference SK Joonas Jälkö, Antti Honkela arXiv preprint arXiv:2110.14426, 2021 | | 2021 |
Graham CormodeMeta AI, University of WarwickVerified email at warwick.ac.uk
