Abstract
In cryptography, secure multi-party computation involves multi-party participant entities, each of them has its own secret input and wants to jointly compute a function through some interaction protocol. In this process, cheating by any of the parties will cause errors in the final result. In order to prevent parties from cheating, many experts have proposed protocols with semi-honest security, malicious security and covert security. However, existing protocols can only detect and deal with malicious attackers after being attacked. This passive approach will not only consume more resources, but also cause excessive losses for participants. To solve the above problems, this paper proposes an optimal participant selection model based on the time-varying entropy. Over time, we evaluate the safety entropy of the secure multi-party computation model. Once the entropy value exceeds the expectation, we will reselect the adversary through the reputation mechanism, thus always ensuring the safe state of the model. Our model can better predict the unsafe state of the system, and the security of the model can be guaranteed by the choice of the adversary. At this same time, it has better efficiency in the design of password protocol.
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Acknowledgments
Our research work is funded by National Natural Science Foun-dation of China (No. 61962009), Major Scientific and Technological Special Project of Guizhou Province (20183001), Talent project of Guizhou Big Data Academy. Guizhou Provincial Key Laboratory of Public Big Data ([2018]01), Foundation of Guizhou Provincial Key Laboratory of Public Big Data (2018BDKFJJ009).
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Zhang, X., Liu, Y., Chen, Y., Wang, Z. (2020). A Secure Multi-party Computational Adversary Selection Model Based on Time-Varying of Entropy. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_50
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DOI: https://doi.org/10.1007/978-3-030-62223-7_50
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