Skip to main content
SpringerLink
Log in

Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things

  • Research
  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

This paper presents the Edge-Based Privacy Preserving Approach (EBPPA) for healthcare applications that store private user data on cloud servers, and perform computation operations for patient diagnoses. The increasing cyber-attacks on hospital systems and the exposure of mathematical operations on cloud-stored data to untrusted entities pose significant data privacy and security risks. To address these challenges, our proposed approach incorporates two key components such as Homomorphic Encryption and the XGBoost algorithm. Homomorphic Encryption protects medical plaintext data, by enabling computations on encrypted data without decryption. This ensures that sensitive patient data remains secure even if attackers access it. Additionally, we employ Secret Sharing, which distributes computations to multiple virtual nodes located on edge. By doing so, all arithmetic operations are masked, preventing untrusted cloud servers from gaining knowledge about the specific tasks performed on the encrypted patient data. This enhances the confidentiality of the data and safeguards against potential privacy breaches. The virtual edge nodes (VENs) in our approach leverage the computational resources of the cloud to handle computationally intensive mathematical functions efficiently. This enables accurate and efficient healthcare data processing and reduces latency in data transmission between devices and edge nodes. To validate the effectiveness of our approach, we conducted a comparative analysis with existing studies. The results demonstrate that storing homomorphically encrypted data at the edge preserves data privacy and integrity while ensuring the confidentiality of the data through secret sharing-based multi-node computation. This approach offers a robust solution for protecting sensitive patient data from unauthorized access and maintaining data confidentiality in untrusted cloud networks. Our proposed Edge Based Privacy Preserving Approach (EBPPA) combines Homomorphic Encryption, which secures medical data, and the XGBoost algorithm, which enables accurate computations, to address healthcare applications’ challenges. By leveraging secret sharing and virtual edge nodes, our approach provides enhanced data privacy, integrity, and confidentiality, making it a promising solution for securing sensitive patient data in the healthcare domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K.K.R., Yang, R., Wang, X.: Lightweight privacy-preserving medical diagnosis in edge computing. IEEE Trans. Serv. Comput. 15(3), 1606–1618 (2020)

    Article  Google Scholar 

  2. Singh, V., Chandna, H., Kumar, A., Kumar, S., Upadhyay, N. and Utkarsh, K.: IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects. EAI Endorsed Trans Internet Things. 6(21), (2020). (Caring Home, 3 Best Medical Alerts Systems for Those with Pacemakers. Triax Technologies, Contact Tracing IoT Solution. COVID-19 Vaccine: The Role of IoT. Cyberattacks Cost Hospitals Millions during COVID-19 As Hospitals Cope with a COVID-19 Surge, Cyber Threats Loom)

  3. Al-Turjman, F., Nawaz, M.H., Ulusar, U.D.: Intelligence in the Internet of Medical Things era: A systematic review of current and future trends. Comput. Commun. 150, 644–660 (2020)

    Article  Google Scholar 

  4. Islam, M.M., Rahaman, A., Islam, M.R.: Development of smart healthcare monitoring system in IoT environment. SN Comput. Sci. 1, 1–11 (2020)

    Article  Google Scholar 

  5. Shuai, M., Liu, B., Yu, N., Xiong, L., Wang, C.: Efficient and privacy-preserving authentication scheme for wireless body area networks. J. Inf. Secur. Appl. 52, 102499 (2020)

    Google Scholar 

  6. Li, B., Zhou, X., Ning, Z., Guan, X., Yiu, K.C.: Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach. Inf. Sci. 612, 384–398 (2022)

    Article  Google Scholar 

  7. Sowjanya, K., Dasgupta, M., Ray, S.: An elliptic curve cryptography based enhanced anonymous authentication protocol for wearable health monitoring systems. Int. J. Inf. Secur. 19, 129–146 (2020)

    Article  Google Scholar 

  8. Li, B., Tan, Y., Wu, A., Duan, G.: A distributionally robust optimization based method for stochastic model predictive control. IEEE Trans. Autom. Control. 67(11), 5762–5776 (2021)

    Article  MathSciNet  Google Scholar 

  9. Salim, M.M., Kim, I., Doniyor, U., Lee, C., Park, J.H.: Homomorphic encryption based privacy-preservation for iomt. Appl. Sci. 11(18), 8757 (2021)

    Article  Google Scholar 

  10. Zhou, X., Zhang, L.: SA-FPN: An effective feature pyramid network for crowded human detection. Appl. Intell. 52(11), 12556–12568 (2022)

    Article  Google Scholar 

  11. Cheng, L., Yin, F., Theodoridis, S., Chatzis, S., Chang, T.: Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling. IEEE Signal Process. Mag. 39(6), (2022)

  12. Almusallam, N., Alabdulatif, A. Alarfaj, F.: Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain. Comput. Math. Methods Med. 2021, (2021)

  13. Javed, A.R., Sarwar, M.U., Beg, M.O., Asim, M., Baker, T., Tawfik, H.: A collaborative healthcare framework for shared healthcare plan with ambient intelligence. HCIS 10, 1–21 (2020)

    Google Scholar 

  14. Singh, V., Chandna, H., Kumar, A., Kumar, S., Upadhyay, N., Utkarsh, K.: IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects. EAI Endorsed Trans. Internet Things 6(21), (2020)

  15. Evans, M., McMillan, R.: Cyberattacks cost hospitals millions during Covid-19. Wall Str. J. (2021)

  16. Salavi, R.R., Math, M.M. and Kulkarni, U.P.: A survey of various cryptographic techniques: From traditional cryptography to fully homomorphic encryption. In Innovations in Computer Science and Engineering: Proceedings of the Sixth ICICSE 2018 (pp. 295–305). Springer Singapore (2019)

  17. Pham, Q.V., Fang, F., Ha, V.N., Piran, M.J., Le, M., Le, L.B., Hwang, W.J., Ding, Z.: A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art. IEEE Access 8, 116974–117017 (2020)

    Article  Google Scholar 

  18. Zheng, Y., Lv, X., Qian, L., Liu, X.: An Optimal BP Neural Network Track Prediction Method Based on a GA– ACO Hybrid Algorithm. J. Mar. Sci. Eng. 10(10), 1399 (2022)

    Article  Google Scholar 

  19. Cao, K., Wang, B., Ding, H., Lv, L., Dong, R., Cheng, T.,... Gong, F.: Improving Physical Layer Security of Uplink NOMA via Energy Harvesting Jammers. IEEE Trans. Inf. Forensic. Secur. 16, 786–799 (2021)

  20. Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L.: Secure Transmission Scheme Based on Joint Radar and Communication in Mobile Vehicular Networks. IEEE Trans. Intell. Transp. Syst. (2023)

  21. Zhang, X., Wang, Y., Yuan, X., Shen, Y., Lu, Z. Wang, Z.: Adaptive Dynamic Surface Control with Disturbance Observers for Battery/Supercapacitor-based Hybrid Energy Sources in Electric Vehicles. IEEE Trans. Transp. Electrification (2022)

  22. Liu, G.: A Q-Learning-based distributed routing protocol for frequency-switchable magnetic induction-based wireless underground sensor networks. Futur. Gener. Comput. Syst. 139, 253–266 (2023)

    Article  Google Scholar 

  23. Tuli, S., Tuli, S., Wander, G., Wander, P., Gill, S.S., Dustdar, S., Sakellariou, R., Rana, O.: Next generation technologies for smart healthcare: Challenges, vision, model, trends and future directions. Internet Technol. Lett. 3(2), e145 (2020)

    Article  Google Scholar 

  24. Qiao, F., Li, Z., Kong, Y.: A Privacy-Aware and Incremental Defense Method Against GAN-Based Poisoning Attack. IEEE Trans. Comput. Soc. Syst. (2023)

  25. Jiang, H., Wang, M., Zhao, P., Xiao, Z., Dustdar, S.: A Utility-Aware General Framework With Quantifiable Privacy Preservation for Destination Prediction in LBSs. IEEE/ACM Trans. Netw. 29(5), 2228–2241 (2021)

    Article  Google Scholar 

  26. Dai, X., Xiao, Z., Jiang, H., Lui, J.C.S.: UAV-Assisted Task Offloading in Vehicular Edge Computing Networks. IEEE Trans. Mob. Comput. (2023)

  27. Wang, Y., Han, X., Jin, S.: MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems. Wirel. Netw. (2022)

  28. Li, J., Deng, Y., Sun, W., Li, W., Li, R., Li, Q.,... Liu, Z.: Resource Orchestration of Cloud-Edge–Based Smart Grid Fault Detection. ACM Trans. Sen. Netw., 18(3), (2022)

  29. El Majdoubi, D., El Bakkali, H., Sadki, S., Maqour, Z., Leghmid, A.: The Systematic Literature Review of Privacy-Preserving Solutions in Smart Healthcare Environment. Secur. Commun. Netw. 2022, (2022)

  30. Tawalbeh, L.A., Muheidat, F., Tawalbeh, M., Quwaider, M.: IoT Privacy and security: Challenges and solutions. Appl. Sci. 10(12), 4102 (2020)

    Article  Google Scholar 

  31. Lin, H., Garg, S., Hu, J., Wang, X., Piran, M.J., Hossain, M.S.: Privacy-enhanced data fusion for COVID-19 applications in intelligent Internet of medical Things. IEEE Internet Things J. 8(21), 15683–15693 (2020)

    Article  Google Scholar 

  32. Vizitiu, A., Niƫă, C.I., Puiu, A., Suciu, C., Itu, L.M.: Applying deep neural networks over homomorphic encrypted medical data. Comput. Math. Methods Med. 2020, (2020)

  33. Cheng, W., Ou, W., Yin, X., Yan, W., Liu, D., Liu, C.: A privacy-protection model for patients. Secur. Commun. Netw. 2020, 1–12 (2020)

    Google Scholar 

  34. Wang, S., Sheng, H., Zhang, Y., Yang, D., Shen, J.,..., Chen, R.: Blockchain-Empowered Distributed Multi-Camera Multi-Target Tracking in Edge Computing. IEEE Trans. Ind. Inform. (2023)

  35. Jiang, H., Dai, X., Xiao, Z., Iyengar, A.K.: Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing. IEEE Trans. Mob. Comput. (2022)

  36. Han, S., Ding, H., Zhao, S., Ren, S., Wang, Z., Lin, J.,..., Zhou, S.: Practical and Robust Federated Learning With Highly Scalable Regression Training. IEEE Trans. Neural Netw. Learn. Syst. (2023)

  37. Liao, Q., Chai, H., Han, H., Zhang, X., Wang, X., Xia, W.,..., Ding, Y.: An Integrated Multi-Task Model for Fake News Detection. IEEE Trans. Knowl. Data Eng. 34(11), 5154–5165 (2022)

  38. Ni, Q., Guo, J., Wu, W., Wang, H.: Influence-Based Community Partition With Sandwich Method for Social Networks. IEEE Trans. Comput. Soc. Syst. 1–12, (2022)

  39. Zhang, J., Liu, Y., Li, Z., Lu, Y.: Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems. IEEE Syst. J., (2023)

  40. Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J.,..., Zhao, Z.: Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach. Futur Gener. Comput. Syst. 145, 77–8 (2023)

  41. Ma, J., Hu, J.: Safe consensus control of cooperative-competitive multi-agent systems via differential privacy. Kybernetika 58(3), 426–439 (2022)

    MathSciNet  Google Scholar 

  42. Zhang, H., Mi, Y., Liu, X., Zhang, Y., Wang, J.,..., Tan, J.: A differential game approach for real-time security defense decision in scale-free networks. Comput. Netw. 224, 109635 (2023)

  43. Cheng, B., Zhu, D., Zhao, S., Chen, J.: Situation-Aware IoT Service Coordination Using the Event-Driven SOA Paradigm. IEEE Trans. Netw. Serv. Manage. 13(2), 349–361 (2016)

    Article  Google Scholar 

  44. Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D.,..., Chen, J.: Situation-Aware Dynamic Service Coordination in an IoT Environment. IEEE/ACM Trans. Networking. 25(4), 2082–2095 (2017)

  45. Zhuang, Y., Jiang, N., Xu, Y., Xiangjie, K., Kong, X.: Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks. Wirel. Commun. Mob. Comput. (2022)

  46. Zhang, Z., Wang, L., Zheng, W., Yin, L., Hu, R.,..., Yang, B.: Endoscope image mosaic based on pyramid ORB. Biomed. Signal Process. Control. 71, 103261 (2022)

  47. Liu, Y., Tian, J., Hu, R., Yang, B., Liu, S., Yin, L.,..., Zheng, W.: Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching. Front. Neurorobotics. (2022)

  48. Lu, S., Yang, B., Xiao, Y., Liu, S., Liu, M., Yin, L.,..., Zheng, W.: Iterative reconstruction of low-dose CT based on differential sparse. Biomed. Signal Process. Control, 79, 104204 (2023)

Download references

Funding

No funding was obtained for this study.

Author information

Authors and Affiliations

  1. School of Computer and Electronic Information, Guangxi University, Nanning, 530004, China

    Lingbin Meng & Daofeng Li

Authors
  1. Lingbin Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daofeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Lingbin Meng: Conceptualization, Methodology, Formal analysis, Supervision, Writing - original draft, Writing - review & editing.

Daofeng Li: Investigation, Data Curation, Validation, Resources, Writing - review & editing,Writing - original draft, Writing - review and editing.

Corresponding author

Correspondence to Lingbin Meng.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, L., Li, D. Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things. J Grid Computing 21, 66 (2023). https://doi.org/10.1007/s10723-023-09695-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-023-09695-6

Keywords

Search

Navigation