Performance Evaluation of Lightweight Cryptosystems on Varied File Types in Cyber Physical Systems

Abstract

Hospital environments are critical in the cyber world because the technologies used there directly impact patients' vital information and the continuity of healthcare. Digital infrastructures such as electronic health records, medical devices and communication systems pose a high risk to patient safety and data privacy. Therefore, cybersecurity in hospitals is a vital requirement to both protect patient privacy and ensure the uninterrupted continuity of healthcare services. Hospitals need secure and efficient encryption methods to protect patient privacy and meet operational requirements when handling large amounts of sensitive data. Traditional encryption methods pose challenges in terms of resource consumption, especially for devices with limited processing power and memory. This paper investigates ChaCha20 and Salsa20 lightweight encryption algorithms in healthcare based on factors such as processing time, memory usage and file type. Five different scenarios were created that can be encountered in a daily hospital environment. In these scenarios, the performance of lightweight encryption algorithms was analyzed. Instead of determining which algorithm is better, the study aims to show which algorithm gives better results in each scenario. ChaCha20 provides stable and reliable performance for small and medium-sized files in resource-constrained environments. Salsa20 performs well for large files and tasks that require fast processing. While both ciphers work effectively in different hospital environment scenarios, systems with cryptographic accelerators are found to significantly improve the performance and scalability of these algorithms. The findings of the study provide valuable insights for the selection and integration of lightweight encryption solutions suitable for different healthcare needs.

Keywords

• Medical cyber physical systems (MCPS)
• Internet of medical things (IoMT)
• Lightweight cryptographic algorithms (LCW)

References

1. [1] Priyadarshini, I., Kumar, R., Tuan, L. M. and Son, L. H., “A new enhanced cybersecurity framework for medical cyber-physical systems”, SICS Software-Intensive Cyber-Physical Systems, 35(3–4): 159–183, (2021). DOI: https://doi.org/10.1007/s00450-021-00427-3
2. [2] Ahmed, M., Johnson, L. and Wang, H., “Securing cyber-physical systems in healthcare: Challenges and solutions”, Journal of Cybersecurity Advances, 8(2): 124–134, (2021). DOI: https://doi.org/10.1016/j.jcybersecadv.2021.05.001
3. [3] Qiu, H., Qiu, M., Liu, M. and Memmi, G., “Secure health data sharing for medical cyber-physical systems for healthcare 4.0”, IEEE Journal of Biomedical and Health Informatics, 24(9): 2499–2505, (2020). DOI: https://doi.org/10.1109/JBHI.2020.2973467
4. [4] Weber, S. B., Stein, S., Pilgermann, M. and Schrader, T., “Attack detection for medical cyber-physical systems: A systematic literature review”, IEEE Access, 11: 41796–41815, (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3270225
5. [5] Kocabas, O., Soyata, T. and Aktas, M. K., “Emerging security mechanisms for medical cyber-physical systems”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(3): 401–416, (2016). DOI: https://doi.org/10.1109/TCBB.2016.2520933
6. [6] Shaikh, T. A., Rasool, T. and Verma, P., “Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions”, Artificial Intelligence in Medicine, 146: 102692, (2023). DOI: https://doi.org/10.1016/j.artmed.2023.102692
7. [7] Nair, M. M. and Tyagi, A. K., “Medical cyber-physical systems and its issues”, Procedia Computer Science, 165: 286–292, (2019). DOI: https://doi.org/10.1016/j.procs.2020.01.059
8. [8] Al-Ghuraybi, H. A., AlZain, M. A. and Soh, B., “Ensuring authentication in medical cyber-physical systems: A comprehensive literature review of blockchain technology integration with machine learning”, Multimedia Tools and Applications, 83(12): 35673–35707, (2024). DOI: https://doi.org/10.1007/s11042-023-17065-3

9. [9] Deebak, B. D. and Hwang, S. O., “A cloud-assisted medical cyber-physical system using a privacy-preserving key agreement framework and a Chebyshev chaotic map”, IEEE Systems Journal, 99: 1–12, (2023). DOI: https://doi.org/10.1109/JSYST.2023.3303460
10. [10] National Institute of Standards and Technology (NIST), “Cryptographic accelerator”, https://csrc.nist.gov/glossary/term/cryptographic_accelerator, Access date: 27.12.2024.
11. [11] Bhardwaj, I., Kumar, A. and Bansal, M., “A review on lightweight cryptography algorithms for data security and authentication in IoTs”, Proceedings of the 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, 504–509, (2017). DOI: https://doi.org/10.1109/ISPCC.2017.8269731
12. [12] Thakor, V. A., Razzaque, M. A. and Khandaker, M. R., “Lightweight cryptography algorithms for resource-constrained IoT devices: A review, comparison and research opportunities”, IEEE Access, 9: 28177–28193, (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3052867
13. [13] El-Hajj, M., Mousawi, H. and Fadlallah, A., “Analysis of lightweight cryptographic algorithms on IoT hardware platform”, Future Internet, 15(2): 54, (2023). DOI: https://doi.org/10.3390/fi15020054
14. [14] Chen, F., Tang, Y., Wang, C., Huang, J., Huang, C., Xie, D., Wang, T. and Zhao, C., “Medical cyber–physical systems: A solution to smart health and the state of the art”, IEEE Transactions on Computational Social Systems, 9(5): 1359–1386, (2021). DOI: https://doi.org/10.1109/TCSS.2021.3122807
15. [15] Ey, N., Ashour, A. S., Shi, F., Fong, S. J. and Tavares, J. M. R., “Medical cyber-physical systems: A survey”, Journal of Medical Systems, 42(1): 1–13, (2018). DOI: https://doi.org/10.1007/s10916-018-0921-x
16. [16] Karatas, M., Eriskin, L., Deveci, M., Pamucar, D. and Garg, H., “Big data for healthcare industry 4.0: Applications, challenges and future perspectives”, Expert Systems with Applications, 200: 116912, (2022). DOI: https://doi.org/10.1016/j.eswa.2022.116912
17. [17] Roy, P. K., Singh, A., Desai, J. V. and Singh, S. K., “Healthcare data security using lightweight protocol for cyber physical systems”, IEEE Transactions on Network Science and Engineering, 10(5): 2597–2606, (2022). DOI: https://doi.org/10.1109/TNSE.2022.3186437
18. [18] Quist-Aphetsi, K. and Xenya, M. C., “Securing medical IoT devices using Diffie-Hellman and DES cryptographic schemes”, Proceedings of the 2019 International Conference on Cyber Security and Internet of Things (ICSIoT), Sousse, 105–108, (2019). DOI: https://doi.org/10.1109/ICSIoT47925.2019.00025
19. [19] Pesaru, S., Mallenahalli, N. K. and Vardhan, B. V., “Lightweight cryptography-based data hiding system for Internet of Medical Things”, International Journal of Healthcare Management, 1–14, (2022). DOI: https://doi.org/10.1080/20479700.2022.2161145
20. [20] Hasan, M. K., Islam, S., Sulaiman, R., Khan, S., Hashim, A.-H. A., Habib, S., Islam, M., Alyahya, S., Ahmed, M. M., Kamil, S., et al., “Lightweight encryption technique to enhance medical image security on Internet of Medical Things applications”, IEEE Access, 9: 47731–47742, (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3061710
21. [21] Jammula, M., Vakamulla, V. M. and Kondoju, S. K., “Secure and scalable Internet of Medical Things using ensemble lightweight cryptographic models”, Proceedings of the 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, 982–987, (2023). DOI: https://doi.org/10.1109/ICSCSS57650.2023.10169857
22. [22] Weng, D., “Performance and energy evaluation of lightweight cryptography for small IoT devices”, Proceedings of the 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, 289–295, (2023). DOI: https://doi.org/10.1109/UEMCON59035.2023.10316062
23. [23] Kumar, P. K. and Mondal, B., “Lightweight stream cipher for healthcare IoT”, Proceedings of the 2023 IEEE 2nd International Conference on Industrial Electronics: Developments & Applications (ICIDeA), Kolkata, 444–449, (2023). DOI: https://doi.org/10.1109/ICIDeA59866.2023.10295196
24. [24] Mohammed, Z. A. and Hussein, K. A., “Lightweight cryptography concepts and algorithms: A survey”, Proceedings of the 2023 Second International Conference on Advanced Computer Applications (ACA), Madurai, 1–7, (2023). DOI: https://doi.org/10.1109/ACA57612.2023.10346914
25. [25] Maram, B., Majji, R., Gopisetty, G. K. D., Garg, A., Daniya, T. and Kumar, B. S., “Lightweight cryptography based deep learning techniques for securing IoT based e-healthcare systems”, Proceedings of the 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, 334–341, (2023). DOI: https://doi.org/10.1109/ICACRS58579.2023.10404726
26. [26] Thilagaraj, M., Murugan, C. A., Ramani, U., Ganesh, C. and Sabarish, P., “A survey of efficient lightweight cryptography algorithms for Internet of Medical Things”, Proceedings of the 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, Vol. 1, 2105–2109, (2023). DOI: https://doi.org/10.1109/ICACCS57279.2023.10112818
27. [27] Baser, M., “IoMT-Scenarios-Dataset”, GitHub repository, https://github.com/meliikebaser/IoMT-Scenarios-Dataset, (2025).
28. [28] Banachewicz, K., “Pfizer documents”, Kaggle dataset, (2022).
29. [29] Albertina, B., Watson, M., Holback, C., Jarosz, R., Kirk, S., Lee, Y., Rieger-Christ, K. and Lemmerman, J., “The cancer genome atlas lung adenocarcinoma collection (TCGA-LUAD) (version 4)”, The Cancer Imaging Archive, (2016). DOI: https://doi.org/10.7937/k9/tcia.2016.jgnihep5
30. [30] Cohen, J. P., Morrison, P. and Dao, L., “COVID-19 image data collection”, arXiv preprint, (2020). DOI: https://doi.org/10.48550/arXiv.2003.11597
31. [31] Borgli, H., Thambawita, V., Smedsrud, P. H., Hicks, S., Jha, D., Eskeland, S. L., Randel, K. R., Pogorelov, K., Lux, M., Nguyen, D. T. D., Johansen, D., Griwodz, C., Stensland, H. K., Garcia-Ceja, E., Schmidt, P. T., Hammer, H. L., Riegler, M. A., Halvorsen, P. and de Lange, T., “HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy”, Scientific Data, 7(1): 283, (2020). DOI: https://doi.org/10.1038/s41597-020-00622-y