American Journal of Bioscience and Bioengineering

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm

Today's medical research is seen to be highly dependent on data exchange; unfortunately, despite its benefits, it frequently encounters problems, particularly issues with data privacy. As a result, several methods and infrastructures have been created to ensure that patients and research participants maintain their anonymity when data is exchanged. However, privacy protection often has a cost, such as limitations on the types of studies that may be done on shared data. The lack of a systematization that would make the trade-offs made by various techniques obvious is what needs to be addressed. In this research, develop the Feline-Storm Based Privacy Preservation Technique for multi-institutional clinical data. Data mining provides many advantages in various domains, particularly in medicine. The data about the disease is ensured to the experts, who can determine the effects, availability, and nature. The private information of the persons should not be disclosed to the expert groups, which ensures the confidentiality of the confidential information. Hence, to ensure the privacy of the people's electronic health records (EHR), this research utilizes the C-mixture and three privacy restraints that strengthen the privacy measures. Furthermore, the Hybrid Feline-storm algorithm, which emphasizes exploitation or the exploration phase at any instance, avoiding the local optima and the premature convergence to ensure the optimized privacy preserved of the data. This research also establishes security strategies such as K-anonymity, T-closeness, and L-diversity to attain complete data privacy. Further, the Feline-storm optimization is developed to minimize information loss. The information loss, class average size, and fitness measure achieved by the proposed methodology are 0.85, 0.38, and 4.7457, respectively.

Hybrid Feline-Storm Algorithm, Data Anonymization, C-mixture, Clinical Trial and Pharma Industry

APA Style

Sagarkumar Patel, Rachna Patel. (2023). Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. American Journal of Bioscience and Bioengineering, 11(5), 79-91. https://doi.org/10.11648/j.bio.20231105.14

ACS Style

Sagarkumar Patel; Rachna Patel. Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. Am. J. BioSci. Bioeng. 2023, 11(5), 79-91. doi: 10.11648/j.bio.20231105.14

AMA Style

Sagarkumar Patel, Rachna Patel. Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. Am J BioSci Bioeng. 2023;11(5):79-91. doi: 10.11648/j.bio.20231105.14

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Liu, Yi, J. Q. James, Jiawen Kang, Dusit Niyato, and Shuyu Zhang. "Privacy-preserving traffic flow prediction: A federated learning approach." IEEE Internet of Things Journal 7, no. 8 (2020): 7751-7763.
2. Zhao, Lingchen, Qian Wang, Qin Zou, Yan Zhang, and Yanjiao Chen. "Privacy-preserving collaborative deep learning with unreliable participants." IEEE Transactions on Information Forensics and Security 15 (2019): 1486-1500.
3. Lu, Yunlong, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang. "Blockchain and federated learning for privacy-preserved data sharing in industrial IoT." IEEE Transactions on Industrial Informatics 16, no. 6 (2019): 4177-4186.
4. Wang, X., Zhang, A., Xie, X., & Ye, X. (2019). Secure‐aware and privacy‐preserving electronic health record searching in cloud environment. International Journal of Communication Systems, 32(8), e3925.
5. Wang, Y., Zhang, A., Zhang, P., & Wang, H. (2019). Cloud-assisted EHR sharing with security and privacy preservation via consortium blockchain. IEEE Access, 7, 136704-136719.
6. Wilkowska, W., & Ziefle, M. (2012). Privacy and data security in E-health: Requirements from the user's perspective. Health Informatics Journal, 18(3), 191-201. doi: 10.1177/1460458212442933.
7. Makhdoom, Imran, Ian Zhou, Mehran Abolhasan, Justin Lipman, and Wei Ni. "PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities." Computers & Security 88 (2020): 101653.
8. Kaissis, Georgios, Alexander Ziller, Jonathan Passerat-Palmbach, ThéoRyffel, DmitriiUsynin, Andrew Trask, Ionésio Lima Jr et al. "End-to-end privacy preserving deep learning on multi-institutional medical imaging." Nature Machine Intelligence 3, no. 6 (2021): 473-484.
9. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4 (pp. 92-104). Springer International Publishing.
10. Sarrab, M., & Alshohoumi, F. (2020). Privacy Concerns in IoT a Deeper Insight into Privacy Concerns in IoT Based Healthcare. International Journal of Computing and Digital Systems, 9 (03). http://dx.doi.org/10.12785/ijcds/090306.
11. Xi, W., & Ling, L. (2016, December). Research on IoT privacy security risks. 2016 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), pp. 259-262, IEEE. doi: 10.1109/iciicii.2016.0069.
12. Xu, H., Russell, T., Coposky, J., Rajasekar, A., Moore, R., de Torcy, A., Wan, M., Shroeder, W and Chen, S. Y. (2017). iRODS primer 2: integrated rule-oriented data system. Synthesis Lectures on Information Concepts, Retrieval, and Services, 9 (3), 1-131.
13. Seliem, M., Elgazzar, K., & Khalil, K. (2018). Towards privacy preserving IoT environments: a survey. Wireless Communications and Mobile Computing. doi: 10.1155/2018/1032761.
14. Shahzad, A., Lee, Y. S., Lee, M., Kim, Y. G., & Xiong, N. (2018). Real-time cloud-based health tracking and monitoring system in designed boundary for cardiology patients. Journal of Sensors, 320278, 15.
15. Sharma, S., Chen, K., & Sheth, A. (2018). Toward practical privacy-preserving analytics for IoT and cloud-based healthcare systems. IEEE Internet Computing, 22 (2), 42-51.
16. Shi, Y. (2011). Brainstorm Optimization Algorithm. International Conference in Swarm Intelligence, 303-309. doi: 10.1007/978-3-642-21515-5_36.
17. Siddiqa, A., Hashem, I. A. T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., & Nasaruddin, F. (2016). A survey of big data management: Taxonomy and state-of-the-art. Journal of Network and Computer Applications, 71, 151-166. doi: 10.1016/j.jnca.2016.04.008.
18. Lexchin, Joel. "Those who have the gold make the evidence: how the pharmaceutical industry biases the outcomes of clinical trials of medications." Science and engineering ethics 18 (2012): 247-261.
19. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. doi: 10.1016/j.jbusres.2016.08.001.
20. Solangi, Z. A., Solangi, Y. A., Chandio, S., bin Hamzah, M. S., & Shah, A. (2018, May). The future of data privacy and security concerns in Internet of Things. 2018 IEEE International Conference on Innovative Research and Development (ICIRD), 1-4, IEEE. doi: 10.1109/ICIRD.2018.8376320.
21. Sonune, S., Kalbande, D., Yeole, A., & Oak, S. (2017, June). Issues in IoT healthcare platforms: A critical study and review. 2017 International Conference on Intelligent Computing and Control (I2C2), 1-5, IEEE.
22. Stergiou, C., Psannis, K. E., Gupta, B. B., & Ishibashi, Y. (2018). Security, privacy & efficiency of sustainable cloud computing for big data & IoT. Sustainable Computing: Informatics and Systems, 19, 174-184. https://doi.org/10.1016/j.suscom.2018.06.003.
23. Stojkov, M., Sladić, G., Milosavljević, B., Zarić, M., & Simić, M. (2019). Privacy concerns in IoT smart healthcare system. Conference: 8th International Conference on Information Society and Technology (ICIST), 1.
24. Sudhakar, R. V., & Rao, T. C. M. (2020). Security aware index based quasi–identifier approach for privacy preservation of data sets for cloud applications. Cluster Computing, 1-11.
25. Suneetha, V., Suresh, S., & Jhananie, V. (2020). A novel framework using Apache spark for privacy preservation of healthcare big data. Proceedings of 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 743-749, IEEE.
26. Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (05), 571-588.
27. Tabassum, K., Ibrahim, A., & El Rahman, S. A. (2019, April). Security issues and challenges in IoT. 2019 International Conference on Computer and Information Sciences (ICCIS), 1-5. doi: 10.1109/ICCISci.2019.8716460.
28. Tariq, N., Asim, M., Al-Obeidat, F., Zubair Farooqi, M., Baker, T., Hammoudeh, M., & Ghafir, I. (2019). The security of big data in fog enabled IoT applications including blockchain: A survey. Sensors, 19 (8), 1788.
29. Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10 (12), 4102.doi: 10.3390/app10124102.
30. Terzi, D. S., Terzi, R., & Sagiroglu, S. (2015). A survey on security and privacy issues in big data. Proceedings of 10th International Conference for Internet Technology and Secured Transactions (ICITST), 202-207. doi: 10.1109/ICITST.2015.7412089.
31. Truta, T. M., & Vinay, B. (2006). Privacy protection: p-sensitive k-anonymity property. Proceedings of IEEE International Conference on Data Engineering Workshops (ICDEW'06), 94-94. doi: 10.1109/ICDEW.2006.116.
32. Tucker, K., Branson, J., Dilleen, M., Hollis, S., Loughlin, P., Nixon, M. J., & Williams, Z. (2016). Protecting patient privacy when sharing patient-level data from clinical trials. BMC Medical Research Methodology, 16(1), 5-14. https://doi.org/10.1186/s12874-016-0169-4.
33. Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2018). Continuous patient monitoring with a patient centric agent: A block architecture. IEEE Access, 6, 32700-32726. doi: 10.1109/ACCESS.2018.2846779.
34. Wachter, S. (2018). Normative challenges of identification on the Internet of Things: Privacy, profiling, discrimination, and the GDPR. Computer Law & Security Review, 34 (3), 436-449. https://doi.org/10.1016/j.clsr.2018.02.002.
35. Li, Xiaoxiao, Yufeng Gu, NichaDvornek, Lawrence H. Staib, Pamela Ventola, and James S. Duncan. "Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results." Medical Image Analysis 65 (2020): 101765.
36. Mirjalili, Seyedali, and SeyedaliMirjalili. "Genetic algorithm." Evolutionary Algorithms and Neural Networks: Theory and Applications (2019): 43-55.
37. Rezaei, Hossein, Omid Bozorg-Haddad, and Xuefeng Chu. "Grey wolf optimization (GWO) algorithm." Advanced optimization by nature-inspired algorithms (2018): 81-91.
38. Zhou, Jian, Shuai Huang, Tao Zhou, Danial JahedArmaghani, and Yingui Qiu. "Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential." Artificial Intelligence Review 55, no. 7 (2022): 5673-5705.
39. Chu, Shu-Chuan, Pei-Wei Tsai, and Jeng-Shyang Pan. "Cat swarm optimization." In PRICAI 2006: Trends in Artificial Intelligence: 9th Pacific Rim International Conference on Artificial Intelligence Guilin, China, August 7-11, 2006 Proceedings 9, pp. 854-858. Springer Berlin Heidelberg, 2006.
40. Shi, Yuhui. "An optimization algorithm based on brainstorming process." In Emerging Research on Swarm Intelligence and Algorithm Optimization, pp. 1-35. IGI Global, 2015.