| The value of collaboration in convex machine learning with differential privacy N Wu, F Farokhi, D Smith, MA Kaafar 2020 IEEE Symposium on Security and Privacy (SP), 304-317, 2020 | 145 | 2020 |
| The cost of privacy in asynchronous differentially-private machine learning F Farokhi, N Wu, D Smith, MA Kaafar IEEE Transactions on Information Forensics and Security 16, 2118-2129, 2021 | 17 | 2021 |
| Fairness and cost constrained privacy-aware record linkage N Wu, D Vatsalan, S Verma, MA Kaafar IEEE Transactions on Information Forensics and Security 17, 2644-2656, 2022 | 6 | 2022 |
| Privacy-Preserving Record Linkage for Cardinality Counting N Wu, D Vatsalan, MA Kaafar, SK Ramesh The 18th ACM ASIA Conference on Computer and Communications Security (ACM …, 2023 | 4 | 2023 |
| Cardinality Counting in “Alcatraz”: A Privacy-aware Federated Learning Approach N Wu, X Yuan, S Wang, H Hu, M Xue ACM The Web Conference 2024 (WWW '24), 2024 | 2 | 2024 |
| Systematic Literature Review of AI-enabled Spectrum Management in 6G and Future Networks B Sabir, S Yang, D Nguyen, N Wu, A Abuadbba, H Suzuki, S Lai, W Ni, ... arXiv preprint arXiv:2407.10981, 2024 | | 2024 |
| Privacy-Preserving Data Sharing with Machine Learning N Wu Macquarie University, 2023 | | 2023 |
| Optimized Data Sharing with Differential Privacy: A Game-theoretic Approach using Federated Learning N Wu, D Smith, MA Kaafar The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), 2021 | | 2021 |
