An Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts

Abstract Cloud computing has become the preferred platform for deploying blockchain-enabled smart contracts because of its scalability and flexibility. However, existing access control mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and conventional blockchain authentication expose sensitive user information during authentication, rely on static authorization policies, and lack intelligent mechanisms for detecting evolving cyber threats. This study proposes an Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts that integrates Modified Groth16 Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning, Differential Privacy, GraphSAGE Graph Neural Networks, Autoencoder-based anomaly detection, Proximal Policy Optimization (PPO), and Blockchain Smart Contracts. The framework enables credential-free authentication, confidential collaborative computation, adaptive authorization, intelligent threat detection, and immutable blockchain auditing without compromising user privacy. The proposed framework was implemented and evaluated using the CICIDS2017 cybersecurity dataset. Experimental results achieved 96.4% privacy preservation, 94.1% security strength, 99.0% execution integrity, 98.7% auditability, 90.3% scalability, 88.6% computational performance, 86.9% cost efficiency, 98.91% validation accuracy, 99.62% ROC-AUC, 94.90% Macro F1-Score, and an overall system fitness of 94.23%. Comparative evaluation against Hawk, Zether, Ekiden, and a Federated Learning-only IDS demonstrated superior performance across all evaluation metrics. The proposed framework therefore provides an intelligent, scalable, and privacy-preserving access control solution suitable for next-generation cloud-based smart contract systems. Keywords: Privacy-Preserving Access Control; Smart Contracts; Cloud Computing; Zero-Knowledge Proof; Secure Multi-Party Computation; Trusted Execution Environment; Federated Learning; Blockchain.

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-13
DOI
https://doi.org/10.5281/zenodo.21915229
Primary Topic
Blockchain Technology Applications and Security
Type
article
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article

An Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts

Bennett E.O., V.I.E. Anireh, Matthias D., C. O. Enuma
Zenodo (CERN European Organization for Nuclear Research)
Blockchain Technology Applications and Security
article

An Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts

Bennett E.O., V.I.E. Anireh, Matthias D., C. O. Enuma
article en

Abstract

Abstract Cloud computing has become the preferred platform for deploying blockchain-enabled smart contracts because of its scalability and flexibility. However, existing access control mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and conventional blockchain authentication expose sensitive user information during authentication, rely on static authorization policies, and lack intelligent mechanisms for detecting evolving cyber threats. This study proposes an Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts that integrates Modified Groth16 Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning, Differential Privacy, GraphSAGE Graph Neural Networks, Autoencoder-based anomaly detection, Proximal Policy Optimization (PPO), and Blockchain Smart Contracts. The framework enables credential-free authentication, confidential collaborative computation, adaptive authorization, intelligent threat detection, and immutable blockchain auditing without compromising user privacy. The proposed framework was implemented and evaluated using the CICIDS2017 cybersecurity dataset. Experimental results achieved 96.4% privacy preservation, 94.1% security strength, 99.0% execution integrity, 98.7% auditability, 90.3% scalability, 88.6% computational performance, 86.9% cost efficiency, 98.91% validation accuracy, 99.62% ROC-AUC, 94.90% Macro F1-Score, and an overall system fitness of 94.23%. Comparative evaluation against Hawk, Zether, Ekiden, and a Federated Learning-only IDS demonstrated superior performance across all evaluation metrics. The proposed framework therefore provides an intelligent, scalable, and privacy-preserving access control solution suitable for next-generation cloud-based smart contract systems. Keywords: Privacy-Preserving Access Control; Smart Contracts; Cloud Computing; Zero-Knowledge Proof; Secure Multi-Party Computation; Trusted Execution Environment; Federated Learning; Blockchain.

Zenodo (CERN European Organization for Nuclear Research)
Rivers State University (NG)
Peace, Justice and strong institutions
Openalex Percentile: Top 3%
Blockchain Technology Applications and Security
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An Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts — Bennett E.O., V.I.E. Anireh, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS