| An empirical analysis of fairness notions under differential privacy AS de Oliveira, C Kaplan, K Mallat, T Chakraborty arXiv preprint arXiv:2302.02910, 2023 | 10 | 2023 |
| A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses CG Kaplan, C Xu, O Marfoq, G Neglia, AS de Oliveira arXiv preprint arXiv:2310.12112, 2023 | 1 | 2023 |
| Federated Learning for Cooperative Inference Systems: The Case of Early Exit Networks C Kaplan, TS Salem, A Rodio, C Xu, G Neglia arXiv preprint arXiv:2405.04249, 2024 | | 2024 |
| Machine learning model training with privacy and explainability T Chakraborty, AS de Oliveira, K Mallat, C Kaplan US Patent App. 17/992,334, 2024 | | 2024 |
| Machine learning models with multi-budget differential privacy AS de Oliveira, C Kaplan US Patent App. 17/678,449, 2023 | | 2023 |
| Trainable differential privacy for machine learning AS de Oliveira, C Kaplan US Patent App. 17/379,310, 2023 | | 2023 |
| Can Data Subject Perception of Privacy Risks Be Useful in a Data Protection Impact Assessment? S Dashti, A Santana de Oliveira, C Kaplan, M Dalcastagnè, S Ranise SCRYPT 2021: 18th International Conference on Security and Cryptography …, 2021 | | 2021 |
