  | Fatma Sumeyra Dogan* , & EL MESTARI, S. Z.*. (2025). Techniques to achieve anonymization of health data: When are they sufficient to be considered as legally complaint? Communications in Computer and Information Science. doi:10.1007/978-3-031-74630-7_27  Peer reviewed* These authors have contributed equally to this work. |
 | EL MESTARI, S. Z.* , Zuziak, M.* , LENZINI, G., & Rinzivillo, S. (2025). Can Contributing More Put You at a Higher Leakage Risk? The Relationship Between Shapley Value and Training Data Leakage Risks in Federated Learning. In Can Contributing More Put You at a Higher Leakage Risk? The Relationship Between Shapley Value and Training Data Leakage Risks in Federated Learning (pp. 275-286). SciTePress. doi:10.5220/0013639000003979 Peer reviewed* These authors have contributed equally to this work. |
  | Poe, R. L., & EL MESTARI, S. Z. (2024). The Conflict Between Algorithmic Fairness and Non-Discrimination: An Analysis of Fair Automated Hiring. Journal of the Association for Computing Machinery. doi:10.1145/3630106.3659015 Peer Reviewed verified by ORBi |
 | EL MESTARI, S. Z., LENZINI, G., & DEMIRCI, H. (February 2024). Preserving data privacy in machine learning systems. Computers and Security, 137, 103605. doi:10.1016/j.cose.2023.103605 Peer Reviewed verified by ORBi |
 | EL MESTARI, S. Z.* , Doğan, F. S.* , & Maria Botes, W. (2023). Technical and Legal Aspects Relating to the (Re)Use of Health Data When Repurposing Machine Learning Models in the EU. In Privacy Symposium 2023 (pp. 33--48). Springer International Publishing. doi:10.1007/978-3-031-44939-0_3 Peer reviewed* These authors have contributed equally to this work. |