WAN环境和小规模数值场景下的安全多方最大值算子优化方法

OPTIMIZATION OF SECURE MULTI-PARTY MAXIMUM OPERATOR FOR WAN ENVIRONMENT AND SMALL-SCALE VALUE SCENARIOS

  • 摘要: 最大值计算是被广泛应用于各种统计学场景的运算,在当前互联网技术与数据科学高速发展的信息时代,互联网上存在并每时每刻都在产生海量的数据,这些数据受到法律法规的保护,因而需要引入安全多方计算技术对这些数据加以利用,而安全多方计算中的最大值算子效率较低。针对该问题,设计并实现WAN环境和小规模数值场景下的安全多方最大值算子优化算法,这两种算法显著提升安全多方最大值算子的效率,WAN环境下提升约30%~46%,小规模数值场景下提升约10%~50%。

     

    Abstract: Maximum computation is widely used in various statistical scenarios. In the information age with the rapid development of Internet technology and data science, massive data exist on the Internet and are generated every moment. These data are protected by laws and regulations, so it is necessary to introduce secure multi-party computing technology to make use of these data, while the maximum operator in secure multi-party computing is inefficient. To solve this problem, we design and implement the optimization algorithms of secure multiparty maximum operator in the WAN environment and the small-scale value scenarios. These two algorithms significantly improve the efficiency of secure multiparty maximum operator, which is about 30%~46% higher in the WAN environment and 10%~50% higher in the small-scale value scenarios.

     

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