Abstract
Membership inference is a powerful attack to privacy databases especially for medical data. Existing attack model utilizes the shadow model to inference the private members in the privacy data-sets and information, which can damage the benefits of the data owners and may cause serious data leakage. However, existing defence are concentrated on the encryption methods, which ignore the inference can also cause the unacceptable loss in the real applications. In this work, we propose a novel inference attack model, which utilizes a shadow model to simulate the division system in the medical database and subsequently infer the members in the medical databases. Moreover, the established shadow inference model can classify the labels of medical data and obtain the privacy members in the medical databases. In contrast with traditional inference attacks, we apply the attack in the medical databases rather than recommendation system or machine learning classifiers. From our extensive simulation and comparison with traditional inference attacks, we can observe the proposed model can achieve the attacks in the medical data with reasonable attack accuracy and acceptable computation costs.
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References
Hai, P.H., Hoa, V.N.: A novel ensemble of support vector machines for improving medical data classification. Engineering Innovations 6710 (2023)
Wang, J., Li, X.: Secure medical data collection in the internet of medical things based on local differential privacy. Electronics 12(2) (2023)
Aneta, P., Elina, V., Witold, M.: Medical data transformations in healthcare systems with the use of natural language processing algorithms. Applied Sciences 13(2) (2023)
Oh, W., Nadkami, G.N.: Federated learning in health care using structured medical data. Advances in Kidney Disease and Health 30(1), 416 (2023)
Yuan, Y., Ma, M., Luo, L., et al.: B-SSMD: a fine-grained secure sharing scheme of medical data based on blockchain. Security and Communication Networks (2022)
Xi, P., Zhang, X., Wang, L., et al.: A review of blockchain-based secure sharing of healthcare data. Applied Sciences 12(15), 7912 (2022)
Ngabo, D., Wang, D., Iwendi, C., et al.: Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things. Electronics 10(17), 2110 (2021)
Lv, Z., Piccialli, F.: The security of medical data on internet based on differential privacy technology. ACM Transactions on Internet Technology (2020)
Yang, W., Wang, S., Hu, J., et al.: Securing mobile healthcare data: a smart card based cancelable finger-vein bio-cryptosystem. IEEE Access 6, 3693936947 (2018)
Patil, A., Ashwini, D.V., Rashmi, P.T.R., et al.: A mobile cloud based approach for secure medical data management. International Journal of Computer Applications 119(5) (2015)
Zhang, G., Liu, B., Zhu, T., et al.: Label-only membership inference attacks and defenses in semantic segmentation models. IEEE Transactions on Dependable and Secure Computing 20(2), 14351449 (2023)
Hu, L., Li, J., Lin, G., et al.: Defending against membership inference attacks with high utility by GAN. IEEE Transactions on Dependable and Secure Computing 20(3) (2023)
Di, W., Saiyu, Q., Yong, Q., et al.: Understanding and defending against White-box membership inference attack in deep learning. Knowledge-Based Systems 259, 110014 (2023)
Kulynych, B., Yaghini, M., Cherubin, G., et al.: Disparate vulnerability to membership inference attacks. Proceedings on Privacy Enhancing Technologies 2022(1) (2021)
Jia, J., Salem, A., Backes, M., et al.: MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples. CoRR, abs/1909.10594 (2019)
Truex, S., Liu, L., Gursoy, M.E., et al.: Demystifying membership inference attacks in machine learning as a service. IEEE Transactions on Services Computing (2019)
Chen, W., Gaoyang, L., Haojun, H., et al.: MIASec: enabling data indistinguishability against membership inference attacks in MLaaS. IEEE Transactions on Sustainable Computing 5(3), 365376 (2019)
Halfond, W.G., Viegas, J., Orso, A.: A classification of SQL-injection attacks and countermeasures. Proceedings of the IEEE International Symposium on Secure Software Engineering. IEEE 1, 13–15 (2006)
Grubbs, P., Lacharité, M.S., Minaud, B., et al.: Learning to reconstruct: Statistical learning theory and encrypted database attacks. 2019 IEEE Symposium on Security and Privacy (SP). IEEE, pp. 1067–1083 (2019)
Fang, M., Damer, N., Boutros, F., et al.: Cross-database and cross-attack iris presentation attack detection using micro stripes analyses. Image Vis. Comput.Comput. 105, 104057 (2021)
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Xu, T., Liu, C., Zhang, K., Zhang, J. (2024). Membership Inference Attacks Against Medical Databases. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_2
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DOI: https://doi.org/10.1007/978-981-99-8138-0_2
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