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In the context of the rapid development of big data, data privacy protection and cybersecurity information on the Internet (Ma et al., 2021; Kou et al., 2021; Chen, Sun, Yang, et al., 2021) have become inevitable issues in the security field, which data from different domains are cross-integrated, each participant forms an interactive scenario for distributed computing in the network communities. This includes cloud computing, edge computing for the Internet of Things (Jerald Nirmal Kumar et al., 2021; Ramu et al., 2020; Li, Wang, Wang, et al., 2020; Wei et al., 2021; Azrour et al., 2021), and secure computation, among others. In the two-party computation, due to insufficient computing power, their own data is encrypted and sent to the second party for computation and return the result value. Multiparty computation occurs when the number of participants increases, and disruption and advocacy actions (Li, Wang, Yang, et al., 2021; Wang, Yang, Bracciali, et al., 2020; Li, Chen, Wang, et al., 2020; Li, Wang, Chen, et al., 2021) occur when multiple parties are involved in the computation. Security and fairness issues will arise during the implementation of the protocol. Integrity and trust (Wang et al., 2021) is an issue that exists among the parties involved, including honest participants, semi-honest participants, and malicious participants (Zhao et al., 2018; Wu et al., 2022; Zhu & Huang, 2022; Zhu, 2021; Gupta & Agarwal, 2021). Malicious participants will deviate from the implementation of the protocol and destroy the protocol in order to maximize their own interests. As in deep learning and machine learning (Chen, Zhang, et al., 2021; Guezzaz et al., 2021), there are many security vulnerabilities and risks of malicious attacks.
Among the three types of participants, the malicious participants have the strongest attack intensity and damage degree. For this type of participant, the current solution is mainly through a trusted third party, and the input is handed over to the trusted third party for calculation. The third parties distribute the corresponding output after calculating. But in many cases, there is no trusted third party, which is similar to the decentralized idea of the blockchain (Wang, Wang, et al., 2020; Li, P., et al., 2020; Zhou et al., 2021). Under the semi-honest model, it is usually not dependent on a trusted third party. The calculation and sending of messages performed by the respective participants raise the issue of security and fairness. This problem is closely related to the subjective consciousness of the participants, and there will also be certain game situations between parties, which may cause the protocol to deviate. In order to eliminate these negative effects, this paper designs an n-round interactive protocol scheme based on information entropy under the semi-honest model to achieve the security and fairness of the SMPC protocol.