Homomorphic secret sharing (HSS) allows multiple input clients to secret-share their data among multiple servers such that each server is able to locally compute a function on its shares to obtain a partial result and all partial results enable the reconstruction of the function's value on the outsourced data by an output client. The existing HSS schemes for {\em high-degree} polynomials either {\em require a large number of servers} or {\em lack verifiability}, which is essential for ensuring the correctness of the outsourced computations. In this paper, we propose a two-server verifiable HSS (VHSS) model and construct a scheme that supports the computation of high-degree polynomials. The degree of the outsourced polynomials can be as high as a polynomial in the system's security parameter. Despite of using only 2 servers, our VHSS ensures that each single server learns no information about the outsourced data and no single server is able to persuade the client to output a wrong function value. Our VHSS is significantly more efficient. When computing degree-7 polynomials, our scheme could be 3-10 times faster than the previously best construction.
翻译:多输入的共享( HSS) 允许多个输入客户在多个服务器中秘密共享数据, 以便让每个服务器能够本地计算其共享的函数, 以获得部分结果, 而所有部分结果都能够让输出客户重建外部数据中的函数值。 现有的 超高度多边共享 HSS 方案, 或需要大量服务器, 或缺乏可核实性, 这对于确保外包计算正确性至关重要 。 在本文中, 我们提出一个两个服务器可核实的 HSS (VHSS) 模型, 并构建一个支持高度多面度计算的方案 。 外包多面值的程度可以像系统安全参数中的一个多面值一样高 。 尽管只使用两个服务器, 我们的 VHSS 方案可以确保每个服务器都得不到关于外包数据的任何信息, 并且没有一个服务器能够说服客户输出错误的函数值 。 我们的 VHSS (VHSS) 模式非常高效 。 在计算高度-7 多元面值时, 之前的构建速度可能比最高 3- 10 次 。