Poisoning Attacks on Fair Machine Learning

Author:

Van Minh-HaoORCID,Du WeiORCID,Wu XintaoORCID,Lu AidongORCID

Publisher

Springer International Publishing

Reference23 articles.

1. Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H.: A reductions approach to fair classification. In: International Conference on Machine Learning, pp. 60–69. PMLR (2018)

2. Barreno, M., Nelson, B., Sears, R., Joseph, A.D., Tygar, J.D.: Can machine learning be secure? In: Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, pp. 16–25 (2006)

3. Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, 26 June–1 July, 2012. icml.cc / Omnipress (2012)

4. Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey. arXiv preprint arXiv:1810.00069 (2018)

5. Chang, H., Nguyen, T.D., Murakonda, S.K., Kazemi, E., Shokri, R.: On adversarial bias and the robustness of fair machine learning. arXiv preprint arXiv:2006.08669 (2020)

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2. Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective;IEEE Transactions on Information Forensics and Security;2024

3. Study on Poisoning Attacks: Application Through an IoT Temperature Dataset;2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE);2023-12-14

4. HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

5. Fairness-Aware Regression Robust to Adversarial Attacks;IEEE Transactions on Signal Processing;2023

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