An Efficient Method to Enhance Health Care Big Data Security in Cloud Computing Using the Combination of Euclidean Neural Network And K-Medoids Based Twin Fish Cipher Cryptographic Algorithm
Keywords:Big data, cloud storage, Filter splash Z normalization, Euclidean neural network, K-medoids based twin fish cipher algorithm
Big data is a phrase that refers to the large volumes of digital data that are being generated as a consequence of technology improvements in the health care industry, e-commerce, and research, among other fields. It is impossible to analyze Big Data using typical analytic tools since traditional data storage systems do not have the capacity to deal with such a large volume of data. Cloud computing has made it more easier for people to store and process data remotely in recent years. By distributing large data sets over a network of cloudlets, cloud computing can address the challenges of managing, storing, and analyzing this new breed of data It's possible for private data to be leaked when it is kept in the cloud, as users have no control over it. This paper proposes a framework for a secure data storage by using the K-medoids-based twin fish cipher cryptographic algorithm. We first normalize the data using the Filter splash Z normalization and then apply the Euclidean neural network to compute similarity, which ensures data correctness and reduces computational cost. As a result, the suggested encryption strategy is used to encrypt and decode the outsourced data, thereby protecting private information from being exposed. The whole experiment was conducted using health data from a large metropolis from the Kaggle database. Using the recommended encryption method, users will be able to maintain their privacy while saving time and money by storing their large amounts of data on the cloud.
M. Elhoseny, A. Abdelaziz, A. S. Salama, A. M. Riad, K. Muhammad, and A. K. Sangaiah, "A hybrid model of internet of things and cloud computing to manage big data in health services applications," Future generation computer systems, vol. 86, pp. 1383-1394, 2018.
L. Hong-Tan, K. Cui-hua, B. Muthu, and C. Sivaparthipan, "Big data and ambient intelligence in IoT-based wireless student health monitoring system," Aggression and Violent Behavior, p. 101601, 2021.
W. Li, Y. Chai, F. Khan, S. R. U. Jan, S. Verma, V. G. Menon, et al., "A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system," Mobile Networks and Applications, vol. 26, pp. 234-252, 2021.
H. Wang, S. Wu, M. Chen, and W. Wang, "Security protection between users and the mobile media cloud," IEEE Communications Magazine, vol. 52, pp. 73-79, 2014.
S. S. Kumar, S. Prasad, M. Parimala, and G. M. Someswar, "Scalable and Secure Sharing of Personal Health records in Cloud Computing Using Attribute Based Encryption," COMPUSOFT: An International Journal of Advanced Computer Technology, vol. 5, 2016.
C. Choi, J. Choi, and P. Kim, "Ontology-based access control model for security policy reasoning in cloud computing," The Journal of Supercomputing, vol. 67, pp. 711-722, 2014.
L. Wei, H. Zhu, Z. Cao, X. Dong, W. Jia, Y. Chen, et al., "Security and privacy for storage and computation in cloud computing," Information sciences, vol. 258, pp. 371-386, 2014.
S. Belguith, N. Kaaniche, M. Laurent, A. Jemai, and R. Attia, "Phoabe: Securely outsourcing multi-authority attribute based encryption with policy hidden for cloud assisted iot," Computer Networks, vol. 133, pp. 141-156, 2018.
P. Sarosh, S. A. Parah, G. M. Bhat, and K. Muhammad, "A security management framework for big data in smart healthcare," Big Data Research, vol. 25, p. 100225, 2021.
A. Jindal, A. Dua, N. Kumar, A. K. Das, A. V. Vasilakos, and J. J. Rodrigues, "Providing healthcare-as-a-service using fuzzy rule based big data analytics in cloud computing," IEEE Journal of Biomedical and Health informatics, vol. 22, pp. 1605-1618, 2018.
G. Manogaran, C. Thota, and M. V. Kumar, "MetaCloudDataStorage architecture for big data security in cloud computing," Procedia Computer Science, vol. 87, pp. 128-133, 2016.
B. Balashunmugaraja and T. Ganeshbabu, "Privacy preservation of cloud data in business application enabled by multi-objective red deer-bird swarm algorithm," Knowledge-Based Systems, vol. 236, p. 107748, 2022.
Z. Li, G. Liu, Y. Dang, Z. Shang, and N. Lin, "Research on New Virtualization Security Protection Management System Based on Cloud Platform," in Journal of Physics: Conference Series, 2022, p. 012010.
J. Du and Y. Pi, "Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data," Security and Communication Networks, vol. 2022, 2022.
A. S. Rajawat, P. Bedi, S. Goyal, R. N. Shaw, A. Ghosh, and S. Aggarwal, "AI and Blockchain for Healthcare Data Security in Smart Cities," in AI and IoT for Smart City Applications, ed: Springer, 2022, pp. 185-198.
R. S. Cordova, R. L. R. Maata, A. S. Halibas, and R. Al-Azawi, "Comparative analysis on the performance of selected security algorithms in cloud computing," in 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2017, pp. 1-4.
M. N. Ul Haq and N. Kumar, "A novel data classification-based scheme for cloud data security using various cryptographic algorithms," International Review of Applied Sciences and Engineering, 2021.
Z. Kartit, A. Azougaghe, H. Kamal Idrissi, M. E. Marraki, M. Hedabou, M. Belkasmi, et al., "Applying encryption algorithm for data security in cloud storage," in International Symposium on Ubiquitous Networking, 2015, pp. 141-154.
X. Li, Y. Chang, G. Ye, X. Gong, and Z. Tang, "GENDA: A Graph Embedded Network Based Detection Approach on encryption algorithm of binary program," Journal of Information Security and Applications, vol. 65, p. 103088, 2022.
S. V. Karuppiah and G. Gurunathan, "Secured storage and disease prediction of E-health data in cloud," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 6295-6306, 2021.
How to Cite
Copyright (c) 2022 International Journal of Computer (IJC)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.