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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 ma ....
In the artificial intelligence era, data-driven computation tasks, such as machine learning, have been playing an essential role as the decision-maker to unlock the value of big data in many fields. Moreover, the ultimate goal of pursuing accuracy and efficiency improvement to better promote the application of data-driven computation has never changed. Since the main method to improve the accuracy of these tasks (e.g., the model training of machine learning) is to increase the diversity of datasets, this requires multiple data providers to share their data. However, data providers, e.g., private companies, are reluctant to share their datasets directly, considering the privacy protection of user information and the leakage prevention of their business secrets. Therefore, how to securely and efficiently perform joint datasets based data-driven computation tasks has become the main problem. In this work, without getting the aid of any trusted-third party (e.g., the cloud server), we cons ....