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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 ....

Committed Oblivious Linear Evaluation , Homomorphic Commitments , Ixed Protocol , Ecure Two Party Computation ,