大数据时代，数据作为生产要素具有重要价值.因此，通过数据共享实现大规模数据的分析挖掘与利用具有重要意义.然而，近年来日益严格的隐私安全保护要求使得数据分散异质的多方之间不能任意共享数据，加剧了“数据孤岛”问题.数据联邦能让多数据拥有方在保护隐私的前提下完成联合查询.因此，基于“数据不动计算动”的联邦计算思想实现了一种多方安全的关系型数据联邦系统.该系统适配多种关系型数据库，能够为用户屏蔽底层多数据拥有方的数据异构性.系统基于秘密共享实现了支持多方安全的基础操作多方安全算子库，优化了算子的结果重建过程，提高了其执行效率.在此基础上，系统支持求和、求均值、求最值、等值连接和任意连接等查询操作，并充分利用多方特点减少各数据拥有方之间的数据交互，降低安全开销，从而有效支持高效数据共享.最后，在标准测试数据集TPC-H上进行实验，实验结果说明：与目前的数据联邦系统SMCQL和Conclave相比，该系统能够支持更多的数据拥有方参与，并且在多种查询操作上有更高的执行效率，最快可超越现有系统3.75倍.

In the era of big data, data is of great value as an essential factor of production. It is of great significance to implement its analysis, mining and utilization of large-scale data via data sharing. However, due to the heterogeneous dispersion of data and increasingly rigorous privacy protection regulations, data owners can not arbitrarily share data. This dilemma turns data owners into data silos. Data Federation calculate collaborative query while preserving the privacy of data silos. This study implements a multi-party secure relational data federation system. The system is designed based on the idea of federated computation that “data stays, computation moves”. Its adaptation interface of the system is different kinds of relational database adaptation, which can shield the data heterogeneity of multiple data owners. The system implements the multi-party security basic calculator library based on secret sharing, and the calculator realizes the optimization of the result reconstruction process. On this basis, it supports the query operations such as sum, average, maximum, equi-join and theta-join. Making full use of the multi-party properties to reduce the data interaction among data owners, the proposed system reduces the security computation overhead, so as to effectively support efficient data sharing. Finally, the experiment is carried out on the benchmark data set TPC-H. The experimental results show that the proposed system can support more data owners’ participation and has higher execution efficiency than current data federation systems such as SMCQL and Conclave by at most 3.75 times.

国家重点研发计划（2018AAA0101100）；国家自然科学基金（61822201，U1811463，62076017，61690202）；北京市科技计划（Z191100002519012）；CCF-华为数据库创新研究计划（CCF-HuaweiDBIR2020008B）；软件开发环境国家重点实验室（北京航空航天大学）开放课题（SKLSDE-2020ZX-07）