A Model for Privacy-Preserving Smart Contracts in Cloud Computing

C. O. Enuma; D. Matthias; V. I. E. Anireh; E. O. Bennett

doi:10.5281/zenodo.21915076

C. O. Enuma Department of Computer Science, Rivers State University, Nigeria.
D. Matthias Department of Computer Science, Rivers State University, Nigeria.
V. I. E. Anireh Department of Computer Science, Rivers State University, Nigeria
E. O. Bennett Department of Computer Science, Rivers State University, Nigeria

DOI: https://doi.org/10.5281/zenodo.21915076

Keywords: Privacy-Preserving Smart Contracts, Cloud Computing, Blockchain, Zero-Knowledge Proofs, Secure Multi-Party Computation, Trusted Execution Environments, Federated Learning, Differential Privacy, Graph Neural Networks, Artificial Intelligence.

Abstract

The increasing adoption of cloud computing and blockchain-based smart contracts has transformed digital service delivery through decentralized automation, transparency, and trusted transaction execution. However, existing smart contract frameworks continue to face challenges related to privacy preservation, secure computation, intelligent access control, execution integrity, and auditability. Most existing solutions rely on isolated privacy-preserving mechanisms, exposing sensitive information during computation and limiting scalability and overall system performance. This study developed a Model for Privacy-Preserving Smart Contract in Cloud Computing by integrating Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning (FL), Differential Privacy (DP), Autoencoder-based anomaly detection, GraphSAGE Graph Neural Networks (GNN), Proximal Policy Optimization (PPO), and Blockchain Smart Contracts within a unified architecture. The study adopted the Design Science Research Methodology (DSRM), while Object-Oriented Analysis and Design (OOAD) guided system implementation. The proposed model was evaluated using the CICIDS2017 cybersecurity benchmark dataset across privacy, security, execution integrity, auditability, scalability, computational performance, and cost efficiency. Experimental results achieved 96% privacy preservation, 94% security strength, 99% execution integrity, 98% auditability, and 90% scalability, while the Artificial Intelligence Privacy Engine attained 98.91% validation accuracy, 0.9962 ROC-AUC, 0.9490 Macro F1-score, and 0.9718 Matthews Correlation Coefficient (MCC). Comparative analysis against RBAC, ABAC, and blockchain-based frameworks demonstrated superior performance in privacy preservation, secure computation, intelligent authorization, and auditability. The proposed model provides a practical, scalable, and intelligent solution for secure smart contract execution in privacy-sensitive cloud computing environments.

Downloads

Download data is not yet available.

References

Alharby, M. (2024). Preserving data secrecy and integrity for cloud storage using smart contracts and cryptographic primitives. Computers, Materials & Continua, 79(2), 2449–2463.

Dalabanjan, G., & Narayan, D. G. (2024). Enabling attribute-based access control for OpenStack cloud resources through smart contracts. 5th International Conference on Innovative Data Communication Technologies. Computer Science 233 (2024) 861–871

Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(1), 5.

Ismayilov, G., & Ozturan, C. (2023). Trustless privacy-preserving data aggregation on Ethereum with hypercube network topology. arXiv preprint arXiv:2308.15267

Joshi, A. (2019). Blockchain for Secure and Efficient Data Sharing in Vehicle-to-Grid Integration. IEEE Transactions on Industrial Informatics, 15(10), 5494-5503.

Kong, B., Yu, W., Wang, G. X., Liang, C., & Ren, K. (2019). Survey on the Security of Data Storage in Cloud Computing. Proceedings of the IEEE, 107(12), 2582-2601.

Kshetri, N. (2017). Can Blockchain Strengthen the Internet of Things? IT Professional, 19(4), 68-72.

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology, Special Publication, 800(145), 7.

Möser, M., & Böhme, R. (2017). On the Security and Privacy of Decentralized File Storage Systems. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS), 639-651.

Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.

Nugent, C. (2021). Addressing the Challenges of the General Data Protection Regulation with Smart Contracts and Blockchain Technology. Computer Law & Security Review, 40, 105556.

Peffers, K., Rothenberger, M., & Kuechler, B. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.

Saberi, S., Kouhizadeh, M., & Sarkis, J. (2019). Blockchain Technology: A Panacea or Pariah for Resources Conservation and Recycling? Resources, Conservation and Recycling, 150, 104428.

Sun, L., Xu, T., Cai, Z., & Liu, X. (2018). Towards Privacy-Preserving and Trustworthy Data Integrity Proofs in Cloud Storage Systems via Blockchain. IEEE Transactions on Services Computing, 11(4), 758-772.

Szabo, N. (1996). Smart Contracts.

Zhang, Y., Zhang, Y., & Wang, C. (2022). Cloud computing: Service models, security issues, and open research challenges. Future Generation Computer Systems, 130, 78–98.