With the rapid development of secure multi-party computation (MPC) over past decades, applications of MPC has been moving from completing simple computation tasks (e.g., private set intersection) to complex computation tasks (e.g., privacy-preserving machine learning). This is an inevitable trend when more strict privacy protection requirements face more complex and large-scale computation such as big data analytics being applied in many fields. Although the complex computation tasks are not easy to be evaluated with one type of MPC protocols from beginning to the end, it can be more efficiently evaluated by decomposing the complex task into many simple sub-tasks and evaluating each of them with the proper type of MPC protocol in sequence. Therefore, we propose a mixed-protocol MPC framework towards complex computation tasks with malicious security in this work. In particular, we utilize the homomorphic commitment technique to construct six types of share conversion protocols in the malicious model. Then, we construct the maliciously secure mixed-protocol MPC framework based on these share conversion protocols. This is the first maliciously secure mixed-protocol MPC framework relying on the standard model, providing a higher security guarantee than all the previous works in the literature. Also, this is the first general mixed-protocol MPC framework for $n$ parties in the malicious model, in comparison to previous works that either only support fixed number of parties in the malicious model, or only handle limited types of share conversions. Furthermore, we provide the theoretical analysis of the computation and communication costs for the six types of share conversion protocols, as an important reference for future developers, who intend to implement some complex computation task by following this mixed-protocol MPC framework with malicious security.

随着过去几十年安全多方计算（MPC）的快速发展，MPC的应用已经从完成简单的计算任务（例如，私有集合交集）转向复杂的计算任务（例如，隐私保护机器学习）。当更严格的隐私保护要求面临更复杂和更大规模的计算时，如大数据分析在许多领域得到应用，这是一个必然趋势。虽然复杂的计算任务不容易用一种类型的 MPC 协议从头到尾进行评估，但可以通过将复杂任务分解为许多简单的子任务并使用适当类型的评估每个子任务来更有效地评估。 MPC 协议按顺序进行。所以，在这项工作中，我们针对具有恶意安全性的复杂计算任务提出了一种混合协议 MPC 框架。特别是，我们利用同态承诺技术在恶意模型中构建了六种类型的共享转换协议。然后，我们基于这些共享转换协议构建了恶意安全的混合协议 MPC 框架。这是第一个依赖标准模型的恶意安全混合协议 MPC 框架，提供了比以往所有文献工作更高的安全保证。此外，这是第一个通用的混合协议 MPC 框架 我们基于这些共享转换协议构建了恶意安全的混合协议 MPC 框架。这是第一个依赖标准模型的恶意安全混合协议 MPC 框架，提供了比以往所有文献工作更高的安全保证。此外，这是第一个通用的混合协议 MPC 框架 我们基于这些共享转换协议构建了恶意安全的混合协议 MPC 框架。这是第一个依赖标准模型的恶意安全混合协议 MPC 框架，提供了比以往所有文献工作更高的安全保证。此外，这是第一个通用的混合协议 MPC 框架$n$恶意模型中的参与方，与之前的工作相比，恶意模型中仅支持固定数量的参与方，或仅处理有限类型的股份转换。此外，我们提供了六种共享转换协议的计算和通信成本的理论分析，作为未来开发人员的重要参考，他们打算通过这种具有恶意安全性的混合协议 MPC 框架来实现一些复杂的计算任务。