| Leakage of dataset properties in {Multi-Party} machine learning W Zhang, S Tople, O Ohrimenko 30th USENIX security symposium (USENIX Security 21), 2687-2704, 2021 | 76* | 2021 |
| Differentially private change-point detection R Cummings, S Krehbiel, Y Mei, R Tuo, W Zhang Advances in Neural Information Processing Systems, 10825-10834, 2018 | 41 | 2018 |
| Attribute privacy: Framework and mechanisms W Zhang, O Ohrimenko, R Cummings Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 35 | 2022 |
| Challenges towards the Next Frontier in Privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ... arXiv preprint arXiv:2304.06929, 2023 | 27 | 2023 |
| Bandit change-point detection for real-time monitoring high-dimensional data under sampling control W Zhang, Y Mei Technometrics 65 (1), 33-43, 2023 | 25 | 2023 |
| Concurrent Composition Theorems for Differential Privacy S Vadhan, W Zhang Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 507–519, 2023 | 15 | 2023 |
| Privately detecting changes in unknown distributions R Cummings, S Krehbiel, Y Lut, W Zhang International Conference on Machine Learning, 2227-2237, 2020 | 8 | 2020 |
| PAPRIKA: Private Online False Discovery Rate Control W Zhang, G Kamath, R Cummings Proceedings of the 38th International Conference on Machine Learning 139 …, 2020 | 6 | 2020 |
| Single and multiple change-point detection with differential privacy W Zhang, S Krehbiel, R Tuo, Y Mei, R Cummings The Journal of Machine Learning Research 22 (1), 1359-1394, 2021 | 4 | 2021 |
| Advancing differential privacy: Where we are now and future directions for real-world deployment R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ... PubPub 6 (1), 2024 | 3 | 2024 |
| Training Private and Efficient Language Models with Synthetic Data from LLMs D Yu, A Backurs, S Gopi, H Inan, J Kulkarni, Z Lin, C Xie, H Zhang, ... Socially Responsible Language Modelling Research, 2023 | 2 | 2023 |
| DP-Fast MH: Private, fast, and accurate Metropolis-Hastings for large-scale Bayesian inference W Zhang, R Zhang International Conference on Machine Learning, 41847-41860, 2023 | 2 | 2023 |
| Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training A Huang, P Liu, R Nakada, L Zhang, W Zhang arXiv preprint arXiv:2306.08173, 2023 | 2 | 2023 |
| Concurrent composition for interactive differential privacy with adaptive Privacy-Loss parameters S Haney, M Shoemate, G Tian, S Vadhan, A Vyrros, V Xu, W Zhang Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications …, 2023 | 1 | 2023 |
| A standardised differential privacy framework for epidemiological modeling with mobile phone data MK Savi, A Yadav, W Zhang, N Vembar, A Schroeder, S Balsari, ... PLOS Digital Health 2 (10), e0000233, 2023 | 1 | 2023 |
| Continual Release of Differentially Private Synthetic Data M Bun, M Gaboardi, M Neunhoeffer, W Zhang arXiv preprint arXiv:2306.07884, 2023 | 1 | 2023 |
| Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size W Zhang, Y Mei, R Cummings International Conference on Artificial Intelligence and Statistics, 11356-11373, 2022 | 1 | 2022 |
| Privacy-preserving Statistical Tools: Differential Privacy and Beyond W Zhang Georgia Institute of Technology, 2021 | 1 | 2021 |
| Membership Inference Attacks and Privacy in Topic Modeling N Manzonelli, W Zhang, S Vadhan arXiv preprint arXiv:2403.04451, 2024 | | 2024 |
Rachel CummingsColumbia UniversityVerified email at columbia.edu
