Abstract
Although federated learning provides strong privacy guarantees when handling cross-device or cross-data center learning tasks, it still faces numerous challenges and potential security threats when applying it to real-world scenarios. This paper proposes a privacy attack identification and protection strategy based on vertical federation clustering, so as to improve privacy protection and data processing security in vertical federation clustering. By fusing parameters, it reduces multi-dimensional data to one-dimensional vector, thus reducing the amount of random disturbance in the subsequent random response process. Moreover, this paper proposes a method of independently setting the answer set for each parameter, which improves the probability of outputting the true value in the random response mechanism. In addition, it improves data utility and clustering precision while ensuring randomness. The comprehensive performance of the model proposed in this paper is excellent in the experiment. In particular, its privacy protection effect reaches 89.34% and 95.14% under ARP and Botnet attacks, respectively. At the same time, the identification rate and recall rate are generally high, showing good privacy protection ability and model robustness. Therefore, the model proposed in this paper improves the privacy protection degree of clustering algorithm in the face of various privacy attacks including data reconstruction attacks under federated learning architecture.
References
Liu, Y., Kang, Y., Zou, T., Pu, Y., He, Y., Ye, X., … and Yang, Q. (2024). Vertical federated learning: Concepts, advances, and challenges. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3615–3634.
Gu, B., Xu, A., Huo, Z., Deng, C., and Huang, H. (2021). Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning. IEEE transactions on neural networks and learning systems, 33(11), 6103–6115.
Novikova, E., Doynikova, E., and Golubev, S. (2022). Federated learning for intrusion detection in the critical infrastructures: Vertically partitioned data use case. Algorithms, 15(4), 104–115.
Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., and Liang, Y. (2021). Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Transactions on Industrial Informatics, 18(6), 4049–4058.
Wu, J. M. T., Teng, Q., Huda, S., Chen, Y. C., and Chen, C. M. (2023). A privacy frequent itemsets mining framework for collaboration in IoT using federated learning. ACM Transactions on Sensor Networks, 19(2), 1–15.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., … and He, B. (2021). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3347–3366.
Zhou, X., Ye, X., Kevin, I., Wang, K., Liang, W., Nair, N. K. C., … and Q. (2023). Hierarchical federated learning with social context clustering-based participant selection for internet of medical things applications. IEEE Transactions on Computational Social Systems, 10(4), 1742–1751.
Li, D., Luo, Z., and Cao, B. (2022). Blockchain-based federated learning methodologies in smart environments. Cluster Computing, 25(4), 2585–2599.
Ouyang, L., Wang, F. Y., Tian, Y., Jia, X., Qi, H., and Wang, G. (2023). Artificial identification: A novel privacy framework for federated learning based on blockchain. IEEE Transactions on Computational Social Systems, 10(6), 3576–3585.
Tian, Y., Zhang, Z., Xiong, J., Chen, L., Ma, J., and Peng, C. (2021). Achieving graph clustering privacy preservation based on structure entropy in social IoT. IEEE Internet of Things Journal, 9(4), 2761–2777.
Kreso, I., Kapo, A., and Turulja, L. (2021). Data mining privacy preserving: Research agenda. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(1), e1392–e1403.
Wang, J., Pal, A., Yang, Q., Kant, K., Zhu, K., and Guo, S. (2022). Collaborative machine learning: Schemes, robustness, and privacy. IEEE Transactions on Neural Networks and Learning Systems, 34(12), 9625–9642.
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., … and Hernandez, J. (2021). Privacy-first health research with federated learning. NPJ digital medicine, 4(1), 132–144.
Domadiya, N., and Rao, U. P. (2021). Improving healthcare services using source anonymous scheme with privacy preserving distributed healthcare data collection and mining. Computing, 103(1), 155–177.
Beltrán, E. T. M., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., … and Celdrán, A. H. (2023). Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges. IEEE Communications Surveys & Tutorials, 25(4), 2983–3013.
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., and Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513–535.
Han, M., Xu, K., Ma, S., Li, A., and Jiang, H. (2022). Federated learning-based trajectory prediction model with privacy preserving for intelligent vehicle. International journal of intelligent systems, 37(12), 10861–10879.
Jie, Z., Chen, S., Lai, J., Arif, M., and He, Z. (2023). Personalized federated recommendation system with historical parameter clustering. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10555–10565.
Singh, S., Rathore, S., Alfarraj, O., Tolba, A., and Yoon, B. (2022). A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Future Generation Computer Systems, 129(2), 380–388.
Zhou, X., Yang, Q., Zheng, X., Liang, W., Kevin, I., Wang, K., … and Jin, Q. (2024). Personalized federated learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse. IEEE Journal on Selected Areas in Communications, 42(4), 817–831.
Shiau, W. L., Wang, X., and Zheng, F. (2023). What are the trend and core knowledge of information security? A citation and co-citation analysis. Information & Management, 60(3), 103774–103788.
Wang, R., and Tsai, W. T. (2022). Asynchronous federated learning system based on permissioned blockchains. Sensors, 22(4), 1672–1684.
Alzubi, J. A., Alzubi, O. A., Singh, A., and Ramachandran, M. (2022). Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Transactions on Industrial Informatics, 19(1), 1080–1087.
Menaga, D., and Saravanan, S. (2022). GA-PPARM: constraint-based objective function and genetic algorithm for privacy preserved association rule mining. Evolutionary Intelligence, 15(2), 1487–1498.
Lee, J., Solat, F., Kim, T. Y., and Poor, H. V. (2024). Federated learning-empowered mobile network management for 5G and beyond networks: From access to core. IEEE Communications Surveys & Tutorials, 26(3), 2176–2212.
Xenakis, A., Chen, Z., and Karabatis, G. (2024). A cluster-based approach for distributed anonymisation of vertically partitioned data. International Journal of Web Engineering and Technology, 19(4), 397–420.
Zhu, X., Wang, D., Pedrycz, W., and Li, Z. (2023). Privacy-preserving realization of fuzzy clustering and fuzzy modeling through vertical federated learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(2), 915–924.
Manzoor, H. U., Shabbir, A., Chen, A., Flynn, D., and Zoha, A. (2024). A survey of security strategies in federated learning: Defending models, data, and privacy. Future Internet, 16(10), 374.
Islam, T. U., Mohammed, N., and Alhadidi, D. (2024). Privacy preserving vertical distributed learning for health data. Journal of Surveillance, Security and Safety, 5(1), 1–18.
Xia, F., and Cheng, W. (2024). A survey on privacy-preserving federated learning against poisoning attacks. Cluster Computing, 27(10), 13565–13582.
Wang, Y., Zheng, W., Liu, Z., Wang, J., Shi, H., Gu, M., and Di, Y. (2023). A federated network intrusion detection system with multi-branch network and vertical blocking aggregation. Electronics, 12(19), 4049–4060.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Journal of Cyber Security and Mobility