Secure Data Acquisition Integration Framework for Key Management and Multilayer Security
Keywords:
Data Security, Key Management, Multilayer Security, Encryption, Data Acquisition, FrameworkAbstract
In the era of digital transformation, the need for robust data security and efficient key management has never been more critical. An organization handling sensitive information across various domains, such as finance, healthcare, and government, requires a comprehensive solution to safeguard their data assets. This abstract introduces a cutting-edge framework designed to address these challenges – the Secured Data Acquisition Integrating Framework (SDAIF) for Key Management and Multilayer Security. SDAIF represents a holistic approach to data security, encompassing data acquisition, encryption, key management, and multilayer security protocols. The framework's primary objective is to provide a unified and extensible system for organizations to protect their data from unauthorized access, tampering, and data breaches. The Secured Data Acquisition Integrating Framework (SDAIF) for Key Management and Multilayer Security represents a comprehensive solution to the increasingly complex data security challenges faced by organizations today. By adopting SDAIF, organizations can strengthen their data protection measures, reduce the risk of data breaches, and ensure the confidentiality, integrity, and availability of their critical data assets.
Downloads
References
S. Yu, C. Wang, K. Ren, “Achieving secure, scalable, and fine-grained data access control in cloud computing,” Proc. IEEE INFOCOM, pp. 1-9, 2019.
J. Bethencourt, A. Sahai, B. Waters, “Ciphertext-policy attribute-based encryption,” Proc. Security and Privacy, pp. 321-334, 2019.
J. Hur, D.K. Noh, “Attribute-based access control with efficient revocation in data outsourcing systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 7 pp. 1214-1221, 2019.
Lewko, B. Waters, “Decentralizing attribute-Based encryption,” Proc. Advances in Cryptology-EUROCRYPT, pp. 568-588, 2019.
M. Li, S. Yu, Y. Zheng, “Scalable and secure sharing of personal health records in cloud computing using attribute-Based Encryption,” IEEE Transactions on Parallel and Distributed System, vol. 24, no. 1, pp. 131- 143, 2019.
Jiang, Y., Wu, S., Yang, H., Luo, H., Chen, Z., Yin, S., & Kaynak, O. (2022). Secure data transmission and trustworthiness judgement approaches against cyber-physical attacks in an integrated data-driven framework. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(12), 7799-7809.
Hu, J., Liang, W., Hosam, O., Hsieh, M. Y., & Su, X. (2022). 5GSS: a framework for 5G-secure-smart healthcare monitoring. Connection Science, 34(1), 139-161.
GARIGIPATI, N., & REDDY, D. V. K. (2023). AN INTEGRATED QUANTUM AND BIOMETRIC KEY GENERATION BASED CLOUD DATA SECURITY FRAMEWORK FOR STRUCTURED AND UNSTRUCTURED ELECTRONIC HEALTH RECORDS. Journal of Theoretical and Applied Information Technology, 101(5).
Poongodi, M., Bourouis, S., Ahmed, A. N., Vijayaragavan, M., Venkatesan, K. G. S., Alhakami, W., & Hamdi, M. (2022). A novel secured multi-access edge computing based vanet with neuro fuzzy systems based blockchain framework. Computer Communications, 192, 48-56.
Ansari, D. B., & Khaliq, M. A. (2022). A Proposed Multilayered Framework for Security and Privacy in Big Data. International Journal of Computer Applications, 975, 8887.
Jadav, N. K., Kakkar, R., Mankodiya, H., Gupta, R., Tanwar, S., Agrawal, S., & Sharma, R. (2023). GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G. Digital Communications and Networks, 9(2), 422-435.
Rahman, M. A., Rahim, M. A., Rahman, M. M., Moustafa, N., Razzak, I., Ahmad, T., & Patwary, M. N. (2022). A secure and intelligent framework for vehicle health monitoring exploiting big-data analytics. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19727-19742.
Kumar, R., Kumar, P., Aljuhani, A., Islam, A. N., Jolfaei, A., & Garg, S. (2022). Deep learning and smart contract-assisted secure data sharing for IoT-based intelligent agriculture. IEEE Intelligent Systems.
Ali, A., Pasha, M. F., Ali, J., Fang, O. H., Masud, M., Jurcut, A. D., & Alzain, M. A. (2022). Deep learning based homomorphic secure search-able encryption for keyword search in blockchain healthcare system: A novel approach to cryptography. Sensors, 22(2), 528.
Leng, J., Chen, Z., Huang, Z., Zhu, X., Su, H., Lin, Z., & Zhang, D. (2022). Secure blockchain middleware for decentralized iiot towards industry 5.0: A review of architecture, enablers, challenges, and directions. Machines, 10(10), 858.
Thuraisingham, B., Kantarcioglu, M., & Khan, L. (2022). Secure Data Science: Integrating Cyber Security and Data Science. CRC Press.
Chaudhary, S., Kakkar, R., Jadav, N. K., Nair, A., Gupta, R., Tanwar, S., ... & Davidson, I. E. (2022). A taxonomy on smart healthcare technologies: Security framework, case study, and future directions. Journal of Sensors, 2022.
Attkan, A., & Ranga, V. (2022). Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security. Complex & Intelligent Systems, 8(4), 3559-3591.
Ramachandra, M. N., Srinivasa Rao, M., Lai, W. C., Parameshachari, B. D., Ananda Babu, J., & Hemalatha, K. L. (2022). An efficient and secure big data storage in cloud environment by using triple data encryption standard. Big Data and Cognitive Computing, 6(4), 101.
Yazdinejad, A., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Karimipour, H. (2023). Secure intelligent fuzzy blockchain framework: Effective threat detection in iot networks. Computers in Industry, 144, 103801.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.