Using information entropy to analyze secure multi-party computation protocol
Title | Using information entropy to analyze secure multi-party computation protocol |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Luo, Yun, Chen, Yuling, Li, Tao, Wang, Yilei, Yang, Yixian |
Conference Name | 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Date Published | oct |
Keywords | Analytical models, Big Data, Computational modeling, Human Behavior, human factors, information entropy, information interaction, Information security, Metrics, multi-party computation, Pervasive Computing Security, Protocols, pubcrawl, resilience, Resiliency, Scalability, secure multi-party computation, security, Semi-Honest Model, Uncertainty |
Abstract | Secure multi-party computation(SMPC) is an important research field in cryptography, secure multi-party computation has a wide range of applications in practice. Accordingly, information security issues have arisen. Aiming at security issues in Secure multi-party computation, we consider that semi-honest participants have malicious operations such as collusion in the process of information interaction, gaining an information advantage over honest parties through collusion which leads to deviations in the security of the protocol. To solve this problem, we combine information entropy to propose an n-round information exchange protocol, in which each participant broadcasts a relevant information value in each round without revealing additional information. Through the change of the uncertainty of the correct result value in each round of interactive information, each participant cannot determine the correct result value before the end of the protocol. Security analysis shows that our protocol guarantees the security of the output obtained by the participants after the completion of the protocol. |
DOI | 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00061 |
Citation Key | luo_using_2021 |
- Pervasive Computing Security
- uncertainty
- Semi-Honest Model
- security
- secure multi-party computation
- Scalability
- Resiliency
- resilience
- pubcrawl
- Protocols
- Analytical models
- multi-party computation
- Metrics
- information security
- information interaction
- information entropy
- Human Factors
- Human behavior
- Computational modeling
- Big Data