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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

A deep learning and blockchain-based security framework for cloud data protection

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pp. 2553–2587Vol. 29Issue 6June 2026DOI: 10.47974/JDMSC-2823 Crossmark XML
Received:
01 Dec 2025
Published Online:
20 Jun 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2823
Pages:
2553–2587

Abstract

Cloud computing provides scalability, cost efficiency and flexibility of numerous applications. Nevertheless, there remain security issues because of data integrity, confidentiality and cyber threats. Cryptographic measures such as encryption and hashing are secure, but do not have the dynamic threat assessment capabilities. This paper introduces a security platform, which combines deep learning with cryptographic hashing and blockchain technology to enrich key exchanges, data verification, and detection of tampering in cloud storage systems. The suggested key exchanged model builds network based key agreement by modifying Diffie-Hellman approach. It uses artificial noise models of neural networks to ensure high security in the key parameters against nefarious activities. The system adopts a CNN-based encryption scheme as well as a decryption system that had been specially developed to offer a flexible encryption security to cloud-based data. The blockchain technology is implemented as a part of the model because it introduces cryptographic hash verification of the stored data to the ledger system that cannot be changed further, as once it is verified it cannot be altered by an unauthorized party. The set of combined Merkle trees results in efficient efficiency improvements on verification processes by reducing the number of computations needed with preserving fast scalable integrity verification algorithms. The proposed model is also tested by performance standard and security analysis in order to determine its efficacy. Findings indicate that a 256-bit key provides the best PSNR of 30.9778 dB and SSIM of 0.8741 at 32x 32 resolution, and ensures the highest visual similarity and structural similarity after decryption.

Keywords

Cloud computingCyber securityIntegrityPrivacyDeep learningBlockchain

Subject Classifications

94A6094A6268T0768M1468P25

References

[1] N. H. Hussein, C. T. Yaw, S. P. Koh, S. K. Tiong, and K. H. Chong, “A comprehensive survey on vehicular networking: Communications, applications, challenges, and upcoming research directions,” IEEE Access, vol. 10, pp. 86127–86180 (2022).
[2] O. Jouini, K. Sethom, A. Namoun, N. Aljohani, M. H. Alanazi, and M. N. Alanazi, “A survey of machine learning in edge computing: Techniques, frameworks, applications, issues, and research directions,” Technologies, vol. 12, no. 6, pp. 81 (2024).
[3] A. I. Abueid, “Big Data and Cloud Computing Opportunities and Application Areas,” Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14509–14516 (2024).
[4] A. Choudhary, P. K. Verma, and P. Rai, “A walkthrough of amazon elastic compute cloud (Amazon EC2): a review,” International Journal for Research in Applied Science and Engineering Technology, vol. 9, no. 11, pp. 93–97 (2021).
[5] A. Choudhary, P. K. Verma, and P. Rai, “The proposed pre-configured deployment model for Amazon EC2 cloud services,” in Proc. 2022 6th International Conference on Electronics, Communication and Aerospace Technology, pp. 794–799 (Dec. 2022).
[6] R. Adee and H. Mouratidis, “A dynamic four-step data security model for data in cloud computing based on cryptography and steganography,” Sensors, vol. 22, no. 3, pp. 1109 (2022).
[7] N. Akhtar, B. Kerim, Y. Perwej, A. Tiwari, and S. Praveen, “A comprehensive overview of privacy and data security for cloud storage,” International Journal of Scientific Research in Science Engineering and Technology., vol. 8, no. 5, pp. 113–152 (2021).
[8] K. Sasikumar and S. Nagarajan, “Comprehensive review and analysis of cryptography techniques in cloud computing,” IEEE Access, vol. 12, pp. 52325–52351 (2024), doi: 10.1109/ACCESS.2024.3385449.
[9] S. K. Mousavi, A. Ghaffari, S. Besharat, and H. Afshari, “Security of internet of things based on cryptographic algorithms: a survey,” Wireless Networks., vol. 27, no. 2, pp. 1515–1555 (2021).
[10] A. Choudhary, “A Short Survey on Comparative Study of Modern Cryptography Approach,” in International Conference on Deep Learning, Artificial Intelligence and Robotics, Cham: Springer Nature Switzerland, pp. 33–48 (2023).
[11] A. Jyoti and R. K. Chauhan, “A blockchain and smart contract-based data provenance collection and storing in cloud environment,” Wireless Networks, vol. 28, no. 4, pp. 1541–1562 (2022).
[12] N. Santos, B. Ghita, and G. L. Masala, “Medical systems data security and biometric authentication in public cloud servers,” IEEE Transactions on Emerging Topics in Computing, vol. 12, no. 2, pp. 572–582 (2024), doi: 10.1109/TETC.2023.3242346.
[13] L. Li, C. Zhou, P. Cong, Y. Shen, J. Zhou, and T. Wei, “Makespan and security-aware workflow scheduling for cloud service cost minimization,” IEEE Transactions on Cloud Computing, vol. 12, no. 2, pp. 609–624 (2024), doi: 10.1109/TCC.2022.3232234.
[14] M. Y. Shakor, M. I. Khaleel, M. Safran, S. Alfarhood, and M. Zhu, “Dynamic AES encryption and blockchain key management: A novel solution for cloud data security,” IEEE Access, vol. 12, pp. 26334–26343 (2024), doi: 10.1109/ACCESS.2024.3367083.
[15] Y. Liu, X. Xing, Z. Tong, X. Lin, J. Chen, Z. Guan, Q. Wu, and W. Susilo, “Secure and scalable cross-domain data sharing in zero-trust cloud-edge-end environment based on sharding blockchain,” IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 2603–2618 (2024), doi: 10.1109/TDSC.2023.3313799. 
[16] W. Li, W. Susilo, C. Xia, L. Huang, F. Guo, and T. Wang, “Secure Data Integrity Check Based on Verified Public Key Encryption With Equality Test for Multi-Cloud Storage,” in IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 6, pp. 5359-5373 (2024).
[17] F. Luo, H. Wang, and X. Yan, “Re-PAEKS: Public-key authenticated re-encryption with keyword search,” IEEE Trans. Mobile Comput., vol. 23, no. 10, pp. 10077–10092 (2024), doi: 10.1109/TMC.2024.3373626.
[18] N. Wang, W. Zhou, J. Wang, Y. Guo, J. Fu, and J. Liu, “Secure and Efficient Similarity Retrieval in Cloud Computing Based on Homomorphic Encryption,” in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 2454-2469 (2024).
[19] P. Shrivastava, B. Alam, and M. Alam, “A hybrid lightweight blockchain based encryption scheme for security enhancement in cloud computing,” Multimed. Tools Appl., vol. 83, no. 1, pp. 2683–2702 (2024).
[20] Y. Song, H. J. Kim, H. J. Lee, and J. W. Chang, “A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing,” Appl. Sci., vol. 14, no. 2, pp. 835 (2024).
[21] S. Zubair and H. M. Ahmed, “A hybrid algorithm-based optimization protocol to ensure data security in the cloud,” Int. J. Inf. Technol., vol. 16, no. 5, pp. 3057–3064 (2024).
[22] M. Akbar, M. M. Waseem, S. H. Mehanoor, and P. Barmavatu, “Blockchain-based cyber-security trust model with multi-risk protection scheme for secure data transmission in cloud computing,” Cluster Comput., vol. 27, no. 7, pp. 9091–9105 (2024).
[23] S. Inam, S. Kanwal, R. Firdous, and F. Hajjej, “Blockchain based medical image encryption using Arnold’s cat map in a cloud environment,” Sci. Rep., vol. 14, no. 1, pp. 5678 (2024).
[24] A. Amaithi Rajan, V. Vetriselvi, M. Raikwar, and R. Balaraman, “SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud,” J. Cloud Comput., vol. 13, no. 1, pp. 139 (2024).
[25] N. Ajay and H. S. Mohan, “A synergistic hybrid SA-PSO-double Qlearning algorithm for dynamic cloud load balancing,” International Journal of Intelligent Engineering & Systems, vol. 18, no. 3, pp. 878–887 (2025), doi: 10.22266/ijies2025.0430.59. 
[26] I. S. Rajesh, B. M. Arikerie, and B. M. Reshmi, “A review on automatic identification of fovea in retinal fundus images,” International Journal of Medical Engineering and Informatics, vol. 12, no. 2, pp. 169–179 (2020), doi: 10.1504/IJMEI.2020.106900.
[27] R. Anitha, “An adaptive virtual machine migration model for optimizing cloud resource utilization,” International Journal of Computing and Information Engineering (IJCIE), vol. 1, no. 3, pp. 22–30 (2026).
[28] K. Siddesha, G. V. Jayaramaiah, and C. Singh, “A novel deep reinforcement learning scheme for task scheduling in cloud computing,” Cluster Computing, vol. 25, no. 6, pp. 4171–4188 (2022), doi: 10.1007/s10586-022-03630-2. 
[29] R. Mehta, S. Mahajan, and K. Khanna, “An MCDM and partial computations based deadline and energy-aware real-time workflow scheduling technique for fog integrated cloud environment,” Journal of Information and Optimization Sciences, vol. 46, no. 4-A, pp. 1091–1103 (2025), doi: 10.47974/JIOS-1894. 
[30] R. K. Gupta and A. P. Mazumdar, “A mathematical congestion-aware Dijkstra optimization model for adaptive routing in CCDN,” Journal of Information and Optimization Sciences, vol. 47, no. 4, pp. 1591–1601 (2026), doi: 10.47974/JIOS-2243.

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