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Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain.
Haijun Bao1, Minghao Yuan2, Haitao Deng2
1School of Computer Science, Qinghai Minzu University, Xining 810007, Qinghai, China.
Heliyon
|December 13, 2024
Summary
This study introduces DHSMPC, a secure multi-party computation protocol using homomorphic encryption to enhance blockchain privacy. DHSMPC offers improved performance and security for complex, private data computations.
Area of Science:
- Computer Science
- Information Security
- Cryptography
Background:
- Blockchain technology is vital but faces privacy challenges.
- Ensuring data immutability and transparency complicates privacy protection.
- Integrating private computing is crucial for blockchain privacy.
Purpose of the Study:
- To design a secure multi-party computation (SMPC) protocol for blockchain privacy.
- To address privacy issues in blockchain using homomorphic encryption.
- To develop a protocol resistant to malicious adversaries.
Main Methods:
- Designed DHSMPC, a secure multi-party computation protocol based on homomorphic encryption.
- Implemented a directed decryption function for enhanced security.
- Utilized ciphertext operations for privacy preservation.
- Combined DHSMPC with blockchain and cloud computing for trusted data management.
Main Results:
- DHSMPC demonstrates smaller ciphertext size and superior performance compared to existing SMPC protocols.
- The protocol enables complex calculations in multi-party settings.
- DHSMPC is proven resistant to semi-malicious attacks, ensuring data security and privacy.
- Authorized users can access decryption results even without participating in computation.
Conclusions:
- DHSMPC effectively enhances blockchain privacy through homomorphic encryption and secure multi-party computation.
- The protocol offers a practical solution for secure and private data management in integrated blockchain and cloud environments.
- DHSMPC provides a robust framework for complex computations while maintaining data confidentiality.
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