Abstract
The security of medical data sharing (MDS) plays an important role in the area of healthcare. Significantly, achieving its security faces more challenges due to the feature of multiparty holding, higher complexity, and serious data silos. Different from traditional secure schemes, which established model cannot deal with the above three problems due to the low accuracy of the MDS secure model, this paper designs a novel secure MDS model and two schemes to increase the accuracy of the model. In detail, to eliminate the issues of data silos and point failure, we combine the federated learning (FL) with blockchain technology into MDS secure model, and the data confidentiality of the exchanged data in the process of FL can be further ensured by differential privacy (DP). Then, to increase the accuracy of the secure MDS model, we design a validation incentive mechanism based on model quality (VIM) and an effective DP method with assigned weights (AWDP), in terms of participants’ enthusiasm and noise accumulation, respectively. Simulations show that the established model is effective and correct and the designed VIM and AWDP can achieve higher accuracy than current popular methods, resulting in 30\(\%\) increment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tang, W., Ren, J., Deng, K., Zhang, Y.: Secure data aggregation of lightweight e-healthcare IOT devices with fair incentives. IEEE Internet Things J. 6(5), 8714–8726 (2019)
Liu, G., Wang, C., Ma, X., Yang, Y.: Keep your data locally: federated-learning-based data privacy preservation in edge computing. IEEE Netw. 35(2), 60–66 (2021)
Qi, S., Lu, Y., Zheng, Y., Li, Y., Chen, X.: Cpds: enabling compressed and private data sharing for industrial internet of things over blockchain. IEEE Trans. Industr. Inf. 17(4), 2376–2387 (2021)
Khan, L.U., Saad, W., Han, Z., Hossain, E., Hong, C.S.: Federated learning for internet of things: recent advances, taxonomy, and open challenges. IEEE Commun. Surv. Tutorials 23(3), 1759–1799 (2021)
Zheng, X., Tian, L., Cai, Z.: A fair and rational data sharing strategy towards two-stage industrial internet of things. IEEE Trans. Industr. Inform. 1 (2022)
Cai, Z., Zheng, X., Wang, J., He, Z.: Private data trading towards range counting queries in internet of things. IEEE Trans. Mob. Comput. 1 (2022)
Yang, H., Zhao, J., Xiong, Z., Lam, K.-Y., Sun, S., Xiao, L.: Privacy-preserving federated learning for UAV-enabled networks: learning-based joint scheduling and resource management. IEEE J. Sel. Areas Commun. 39(10), 3144–3159 (2021)
Zhou, X., Liang, W., She, J., Yan, Z., Wang, K.I.-K.: Two-layer federated learning with heterogeneous model aggregation for 6G supported internet of vehicles. IEEE Trans. Veh. Technol. 70(6), 5308–5317 (2021)
Zheng, X., Cai, Z.: Privacy-preserved data sharing towards multiple parties in industrial IoTs. IEEE J. Sel. Areas Commun. 38(5), 968–979 (2020)
Zhao, Y., et al.: Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet Things J. 8(3), 1817–1829 (2021)
Liu, Y., Peng, J., Kang, J., Iliyasu, A.M., Niyato, D., El-Latif, A.A.A.: A secure federated learning framework for 5G networks. IEEE Wirel. Commun. 27(4), 24–31 (2020)
Jiang, C., Xu, C., Zhang, Y.: PPFL: privacy-preserving federated learning with membership proof. Inf. Sci. 576, 288–311 (2021)
Cai, Z., He, Z.: Trading private range counting over big IoT data. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 144–153 (2019)
Gao, G., Xiao, M., Wu, J., Huang, L., Hu, C.: Truthful incentive mechanism for nondeterministic crowdsensing with vehicles. IEEE Trans. Mob. Comput. 17(12), 2982–2997 (2018)
Zhao, B., Tang, S., Liu, X., Zhang, X.: Pace: privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing. IEEE Trans. Mob. Comput. 20(5), 1924–1939 (2021)
Chen, W., Chen, Y., Chen, X., Zheng, Z.: Toward secure data sharing for the IoV: a quality-driven incentive mechanism with on-chain and off-chain guarantees. IEEE Internet Things J. 7(3), 1625–1640 (2020)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (61771289, 61832012), the Natural Science Foundation of Shandong Province with Grants ZR2021QF050, ZR2021MF075, Shandong Natural Science Foundation Major Basic Research (ZR2019ZD10), Shandong Key Research and Development Program (2019GGX1050), and Shandong Major Agricultural Application Technology Innovation Project (SD2019NJ007).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, J., Zhang, H., Li, G., Yu, K. (2022). Increasing the Accuracy of Secure Model for Medical Data Sharing in the Internet of Things. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-19208-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19207-4
Online ISBN: 978-3-031-19208-1
eBook Packages: Computer ScienceComputer Science (R0)