| Privatesql: a differentially private sql query engine I Kotsogiannis, Y Tao, X He, M Fanaeepour, A Machanavajjhala, M Hay, ... Proceedings of the VLDB Endowment 12 (11), 1371-1384, 2019 | 132 | 2019 |
| Benchmarking differentially private synthetic data generation algorithms Y Tao, R McKenna, M Hay, A Machanavajjhala, G Miklau arXiv preprint arXiv:2112.09238, 2021 | 100 | 2021 |
| R2t: Instance-optimal truncation for differentially private query evaluation with foreign keys W Dong, J Fang, K Yi, Y Tao, A Machanavajjhala Proceedings of the 2022 International Conference on Management of Data, 759-772, 2022 | 38 | 2022 |
| Computing local sensitivities of counting queries with joins Y Tao, X He, A Machanavajjhala, S Roy Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 34 | 2020 |
| Architecting a Differentially Private SQL Engine. I Kotsogiannis, Y Tao, A Machanavajjhala, G Miklau, M Hay CIDR, 2019 | 29 | 2019 |
| Dpgraph: A benchmark platform for differentially private graph analysis S Xia, B Chang, K Knopf, Y He, Y Tao, X He Proceedings of the 2021 International Conference on Management of Data, 2808 …, 2021 | 11 | 2021 |
| Dpxplain: Privately explaining aggregate query answers Y Tao, A Gilad, A Machanavajjhala, S Roy arXiv preprint arXiv:2209.01286, 2022 | 9 | 2022 |
| Prior-aware distribution estimation for differential privacy Y Tao, J Bater, A Machanavajjhala arXiv preprint arXiv:2106.05131, 2021 | 4 | 2021 |
| Explaining Differentially Private Query Results with DPXPlain T Wang, Y Tao, A Gilad, A Machanavajjhala, S Roy Proceedings of the VLDB Endowment 16 (12), 3962-3965, 2023 | 2 | 2023 |
| {ABSet}: Harnessing Blowfish Privacy for Private Friend Recommendations Y Tao, I Kotsogiannis | | 2024 |
| Answering and Explaining SQL Queries Privately Y Tao Duke University, 2022 | | 2022 |
