banner
Home > Open Journal Systems

Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption

Smiley Gandhi, T. Poongodi, K. Sampath Kumar

Abstract


Cardiac disease kills most people worldwide. Predicting and monitoring cardiac problems early improves disease treatment and patient outcomes. The Internet of Medical Things (IoMT) can monitor and analyze physiological data in real-time, changing healthcare. Many researchers find data generation problematic. Encryption is needed to secure a massive amount of data. This paper presents a Twin-Shield Encryption (TSE) that combines Elliptic Curve Cryptography (HECC) and Rivest-Shamir-Adleman (RSA) IoMT assistance for heart illness patient monitoring. Cleveland cardiac dataset from the University of California Irvine (UCI) research repository is collected. It has 12 qualities and 303 occurrences. The data is pre-processed using normalization; feature extracted using Principal Component Analysis (PCA), and securely transmitted to the cloud infrastructure for further processing and analysis. TSE encrypts patient data to prevent unauthorized access and maintain data integrity during transmission and storage. The framework could enhance cardiac ailment diagnosis, treatment, and management by giving clinicians and patients individualized care based on physiological profiles.


Keywords


cardiac disease prediction; Internet of Medical Things (IoMT); Principal Component Analysis (PCA); Elliptic Curve Cryptography (ECC); Rivest-Shamir-Adleman (RSA); Twin Shield Encryption

Full Text:

PDF

References


1. Mishra S, Tyagi AK. The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications. Artificial Intelligence-based Internet of Things Systems. Published online 2022: 105-135. doi: 10.1007/978-3-030-87059-1_4

2. Guan J, Irizawa J, Morris A. Extended Reality and Internet of Things for Hyper-Connected Metaverse Environments. 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Published online March 2022. doi: 10.1109/vrw55335.2022.00043

3. Chanak P, Banerjee I. Internet-of-Things-Enabled SmartVillages: An Overview. IEEE Consumer Electronics Magazine. 2021, 10(3): 12-18. doi: 10.1109/mce.2020.3013244

4. Adhikari M, Hazra A, Menon VG, et al. A Roadmap of Next-Generation Wireless Technology for 6G-Enabled Vehicular Networks. IEEE Internet of Things Magazine. 2021, 4(4): 79-85. doi: 10.1109/iotm.001.2100075

5. Kadhim KT, Alsahlany AM, Wadi SM, et al. An Overview of Patient’s Health Status Monitoring System Based on Internet of Things (IoT). Wireless Personal Communications. 2020, 114(3): 2235-2262. doi: 10.1007/s11277-020-07474-0

6. Karami Z, Hines A, Jahromi HZ. Leveraging IoT Lifelog Data to Analyse Performance of Physical Activities. 2021 32nd Irish Signals and Systems Conference (ISSC). Published online June 10, 2021. doi: 10.1109/issc52156.2021.9467846

7. Mudawi NA. Integration of IoT and Fog Computing in Healthcare Based the Smart Intensive Units. IEEE Access. 2022, 10: 59906-59918. doi: 10.1109/access.2022.3179704

8. Ahmad RW, Salah K, Jayaraman R, et al. Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies. IEEE Access. 2021, 9: 44905-44927. doi: 10.1109/access.2021.3066503

9. Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health. 2020, 20(1). doi: 10.1186/s12889-020-09301-4

10. Preethi S, Priyadharsini C. Deep Learning with Blockchain Technology for Secure Data Management in Healthcare Sector using Hybrid Elliptic Curve-Rivest–Shamir–Adleman Cryptography. Cybernetics and Systems. Published online December 9, 2022: 1-37. doi: 10.1080/01969722.2022.2151187

11. Rana A, Chakraborty C, Sharma S, et al. Internet of Medical Things-Based Secure and Energy-Efficient Framework for Health Care. Big Data. 2022, 10(1): 18-33. doi: 10.1089/big.2021.0202

12. Verma G. Blockchain-based privacy preservation framework for healthcare data in cloud environment. Journal of Experimental & Theoretical Artificial Intelligence. Published online November 21, 2022: 1-14. doi: 10.1080/0952813x.2022.2135611

13. Irshad RR, Alattab AA, Alsaiari OAS, et al. An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations. Healthcare. 2023, 11(4): 580. doi: 10.3390/healthcare11040580

14. Kumar M, Kavita, Verma S, et al. ANAF-IoMT: A Novel Architectural Framework for IoMT-Enabled Smart Healthcare System by Enhancing Security Based on RECC-VC. IEEE Transactions on Industrial Informatics. 2022, 18(12): 8936-8943. doi: 10.1109/tii.2022.3181614

15. Sun Y, Lo FPW, Lo B. Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey. IEEE Access. 2019, 7: 183339-183355. doi: 10.1109/access.2019.2960617

16. Bikku T, Sree KPNVS, Jarugula J, et al. A Novel Integrated IoT Framework with Classification Approach for Medical Data Analysis. 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). Published online March 23, 2022. doi: 10.23919/indiacom54597.2022.9763297

17. Hasan MK, Islam S, Sulaiman R, et al. Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications. IEEE Access. 2021, 9: 47731-47742. doi: 10.1109/access.2021.3061710

18. Bahache AN, Chikouche N, Mezrag F. Authentication Schemes for Healthcare Applications Using Wireless Medical Sensor Networks: A Survey. SN Computer Science. 2022, 3(5). doi: 10.1007/s42979-022-01300-z

19. Younas MS. Effective Heart Disease Prediction using Machine Learning and Data Mining Techniques. Int. Res. J. Eng. Technol. 2021, 8: 3539-3546.

20. Verma P, Sood SK. Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes. IEEE Internet of Things Journal. 2018, 5(3): 1789-1796. doi: 10.1109/jiot.2018.2803201

21. Awotunde JB, Folorunso SO, Ajagbe SA, et al. AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems. Machine Learning for Critical Internet of Medical Things. Published online 2022: 229-254. doi: 10.1007/978-3-030-80928-7_10

22. Wagan SA, Koo J, Siddiqui IF, et al. A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection. Journal of King Saud University - Computer and Information Sciences. 2023, 35(1): 131-144. doi: 10.1016/j.jksuci.2022.11.007

23. Singh LK, Khanna M, singh R. A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction. Measurement. 2023, 221: 113525. doi: 10.1016/j.measurement.2023.113525

24. Singh LK, Khanna M, Thawkar S, et al. A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images. Multimedia Tools and Applications. Published online October 21, 2023. doi: 10.1007/s11042-023-17081-3

25. Singh LK, Khanna M, Singh R. Efficient feature selection for breast cancer classification using soft computing approach: A novel clinical decision support system. Multimedia Tools and Applications. Published online October 16, 2023. doi: 10.1007/s11042-023-17044-8

26. Singh LK, Khanna M, Garg H, et al. Emperor penguin optimization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Computing. Published online May 27, 2023. doi: 10.1007/s00500-023-08449-6




DOI: https://doi.org/10.32629/jai.v7i2.1322

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Smiley Gandhi, T. Poongodi, K. Sampath Kumar

License URL: https://creativecommons.org/licenses/by-nc/4.0/