Comprehensive Cloud Solution for Secure Text Transmission: Enhancing Privacy and Integrity in Digital Communication
Keywords:
Secure text transmission, cloud computing, data privacy, encryption, data integrity, hybrid encryption, multi-factor authentication, role-based access control, regulatory compliance, digital communicationAbstract
Given the increasing risks to data integrity and privacy in the current digital world, it is imperative that text data be transmitted securely over cloud platforms. Although cloud computing provides a scalable and economical infrastructure, it also puts private data at risk from cyberattacks, illegal access, and data breaches. Through the use of cutting-edge encryption algorithms, secure authentication mechanisms, and data integrity verification methods, this article offers a comprehensive cloud solution intended to improve the privacy and integrity of digital communication. By using a multi-layered security strategy and end-to-end encryption, the solution reduces the possibility of illegal parties intercepting data whether it is in transit or at rest. A hybrid encryption paradigm is used to increase security, combining symmetric encryption for effective data protection with asymmetric encryption for key exchange. This guarantees that potential attackers cannot understand the data, even if it is intercepted. Furthermore, data integrity is preserved by the use of secure hashing methods, which enable recipients to confirm the accuracy and completeness of the information they have received. Role-based access control (RBAC) and multi-factor authentication (MFA), which limit access to authorized users and provide accountability, are complementary to this architecture. Because the suggested solution is cloud-agnostic, it may be deployed easily across different cloud providers while upholding uniform security standards. Performance tests show that there is little impact on latency, indicating that the method is viable for real-time applications. Additionally, the system is made to adhere to legal requirements like GDPR and HIPAA, which addresses privacy issues and boosts user confidence. This all-inclusive method of secure text transmission is appropriate for sectors with strict security requirements, such as government, healthcare, and finance, since it not only protects data but also strengthens the dependability and integrity of digital communications in a cloud environment.
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Aljawarneh, S., Alzahrani, A., & Alfaris, H. (2019). A hybrid encryption algorithm for cloud security. Journal of Cloud Computing: Advances, Systems, and Applications, 8(1), 1-14. https://doi.org/10.1186/s13677-019-0150-6
Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120-126. https://doi.org/10.1145/359340.359342
Aljawarneh, S., Alzahrani, A., & Alfaris, H. (2021). Hybrid encryption techniques for cloud environments. Journal of Cloud Computing: Advances, Systems and Applications, 10(1), 1-15. https://doi.org/10.1186/s13677-021-00258-4
Subramanian, L., & Natarajan, A. (2018). Efficiency of hybrid encryption in cloud security. International Journal of Computer Applications, 179(9), 1-6. https://doi.org/10.5120/ijca2018917903
Boneh, D., & Franklin, M. (2001). Identity-based encryption from the Weil pairing. SIAM Journal on Computing, 32(3), 586-615. https://doi.org/10.1137/S0097539701383665
Abdullah, F., & Karim, A. (2020). Applications of identity-based encryption in cloud security. International Journal of Cloud Computing and Services Science, 9(2), 57-65. https://doi.org/10.11591/ijccs.v9i2.8953
Mahmood, T., & Gupta, P. (2020). Multi-factor authentication in cloud services. International Journal of Computer Applications, 179(11), 9-13. https://doi.org/10.5120/ijca2020917270
Haque, M., & Sattar, M. (2021). Role of biometrics in secure digital communication. International Journal of Security and Its Applications, 15(1), 17-26. https://doi.org/10.6025/ijsia.2021.15.1.17
Alotaibi, Y., & Alharkan, I. (2019). The effectiveness of MFA in cloud-based security. Cloud Computing Research and Applications, 8(4), 122-136. https://doi.org/10.11648/j.ccra.2019.08.04.15
Sandhu, R., & Ferraiolo, D. (1996). Role-based access control models. IEEE Computer, 29(2), 38-47. https://doi.org/10.1109/2.485845
Ferraiolo, D., Sandhu, R., & Gavrila, S. (2001). Integrating RBAC with attribute-based models. ACM Transactions on Information and System Security, 4(4), 337-375. https://doi.org/10.1145/503445.503447
Hu, V.C., Ferraiolo, D., & Kuhn, D.R. (2015). A review of access control models for cloud computing. Journal of Cloud Computing: Advances, Systems and Applications, 4(1), 7-26. https://doi.org/10.1186/s13677-015-0027-z
Menezes, A., Oorschot, P. C., & Vanstone, S. (1996). Handbook of Applied Cryptography. CRC Press. ISBN: 978-0849385230
Merkle, R. C. (1989). A digital signature based on a conventional encryption function. Journal of Cryptology, 1(1), 7-20. https://doi.org/10.1007/BF00202994
Svantesson, D., & Clarke, R. (2017). Data privacy regulation and implications for cloud computing. International Journal of Law and Information Technology, 25(4), 332-350. https://doi.org/10.1093/ijlit/eax008
Solove, D.J. (2020). Understanding GDPR’s impact on cloud data protection. Harvard Law Review, 133(2), 456-489.
Kuo, M.-H. (2011). Data governance in HIPAA and cloud-based health information. International Journal of Medical Informatics, 80(12), 840-854. https://doi.org/10.1016/j.ijmedinf.2011.10.001
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf
Zheng, Z., Xie, S., & Dai, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), 557-564. https://doi.org/10.1109/BigDataCongress.2017.89
Al-Saadi, M., & Kumar, V. (2021). Blockchain for secure cloud key management. Journal of Cloud Computing: Advances, Systems and Applications, 10(1), 1-13. https://doi.org/10.1186/s13677-021-00252-w
Jain, A., & Kumar, R. (2019). Machine learning in cloud security: Real-time threat detection. Journal of Machine Learning Research, 20(100), 1-26. https://www.jmlr.org/papers/volume20/19-1114/19-1114.pdf
Bost, R., & Gupta, S. (2015). Privacy-preserving machine learning for secure text analysis. International Journal of Computer Applications, 118(16), 1-6. https://doi.org/10.5120/ijca2015907235
Goodfellow, I., Bengio, Y., & Courville, A. (2018). Deep Learning. MIT Press. ISBN: 9780262035613
Zhang, J., & Liu, L. (2021). Anomaly detection in cloud environments using machine learning. Journal of Cloud Computing: Advances, Systems and Applications, 10(2), 1-12. https://doi.org/10.1186/s13677-021-00256-6
Wang, L., & Zhang, Y. (2020). Privacy-preserving techniques for cloud-based communication systems. International Journal of Security and Privacy, 14(5), 31-45. https://doi.org/10.1504/IJSP.2020.1003059
Liu, W., & Yung, M. (2017). Secure cloud storage through hybrid encryption models. International Journal of Cloud Computing and Services Science, 6(4), 25-37. https://doi.org/10.11591/ijccs.v6i4.7895
He, S., & Zhang, F. (2020). Cloud data security: Protecting sensitive information in transit. Journal of Cloud Computing: Advances, Systems and Applications, 9(3), 45-61. https://doi.org/10.1186/s13677-020-00247-2
Zhou, J., & Huang, Z. (2021). Efficient hybrid encryption algorithms for cloud computing security. International Journal of Cloud Computing and Services Science, 9(5), 112-125. https://doi.org/10.11591/ijccs.v9i5.9267
Li, S., & Wang, Q. (2019). Role of blockchain technology in cloud security. IEEE Transactions on Cloud Computing, 7(4), 1-12. https://doi.org/10.1109/TCC.2019.2895310
Xu, S., & Xu, Q. (2020). Privacy-enhancing technologies for secure text communication in cloud environments. Journal of Information Security and Applications, 55, 102586. https://doi.org/10.1016/j.jisa.2020.102586
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