International Journal of Computer Networks and Applications (IJCNA)

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ISSN : 2395-0455

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

An Application-Layer Framework for Privacy Blockchain Transactions and Smart Contract

Author NameAuthor Details

Souhail Mssassi, Anas Abou El Kalam

Souhail Mssassi[1]

Anas Abou El Kalam[2]

[1]National School of Applied Sciences, Cadi Ayyad University, Morocco.

[2]National School of Applied Sciences, Cadi Ayyad University, Morocco.

Cite this Article

DOI: 10.22247/ijcna/2025/05

Pages : 62-76

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Abstract

While offering transparency and decentralization, Open blockchain networks inadvertently expose user identities and sensitive transaction details. Existing privacy solutions often focus on simple token transfers (e.g., mixers) but fail to protect more complex operations such as smart contract executions. This paper tackles these challenges by introducing a novel application-layer framework anonymizing token transactions and smart contract calls. Building on the principles of Tornado Cash, the approach pools user transactions off-chain, obscuring the link between senders, recipients, and contract interactions. Zero-knowledge proofs were integrated to ensure verifiability without revealing underlying data, all without altering network or consensus mechanisms. Further, a sustainable incentive model is proposed that compensates relayers and executors for gas fees and computational effort, maintaining economic viability. The results indicate that the framework is scalable and platform-agnostic and significantly improves privacy for decentralized applications, mitigating identity exposure and transaction traceability in modern blockchain ecosystems.

Index Terms

Blockchain Privacy

Zero-Knowledge Proof

Blockchain Security

Smart Contract Privacy

Anonymity

Transaction Mixing

Off-Chain Transaction Pooling

Incentive Mechanisms

Multi-Party Computation

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