论文标题
分布分裂：一种随机诱饵方案用于私人计算
Splintering with distributions: A stochastic decoy scheme for private computation
论文作者
论文摘要
在当今分布式机器学习解决方案中，执行计算是一个重要的问题。 Consider the following two set ups between a client and a server, where in setup i) the client has a public data vector $\mathbf{x}$, the server has a large private database of data vectors $\mathcal{B}$ and the client wants to find the inner products $\langle \mathbf{x,y_k} \rangle, \forall \mathbf{y_k} \ in \ Mathcal {B} $。客户端不希望服务器学习$ \ mathbf {x} $，而服务器不希望客户端在其数据库中学习记录。这与另一个设置ii）ii）客户只想在其数据上执行操作，例如在其数据矩阵$ \ mathbf {m} $上计算矩阵倒数的计算，但要使用服务器的卓越计算能力来执行此操作，而无需将服务器泄漏$ \ mathbf {m} $。 \ par我们提出了一种随机方案，用于将客户数据分为私有化的共享，这些共享将在此类设置中传输到服务器。服务器对这些共享执行请求的操作，而不是在服务器上的原始客户端数据上执行所请求的操作。获得的中间结果将发送回客户端由客户组装的客户以获取最终结果。
Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions. Consider the following two set ups between a client and a server, where in setup i) the client has a public data vector $\mathbf{x}$, the server has a large private database of data vectors $\mathcal{B}$ and the client wants to find the inner products $\langle \mathbf{x,y_k} \rangle, \forall \mathbf{y_k} \in \mathcal{B}$. The client does not want the server to learn $\mathbf{x}$ while the server does not want the client to learn the records in its database. This is in contrast to another setup ii) where the client would like to perform an operation solely on its data, such as computation of a matrix inverse on its data matrix $\mathbf{M}$, but would like to use the superior computing ability of the server to do so without having to leak $\mathbf{M}$ to the server. \par We present a stochastic scheme for splitting the client data into privatized shares that are transmitted to the server in such settings. The server performs the requested operations on these shares instead of on the raw client data at the server. The obtained intermediate results are sent back to the client where they are assembled by the client to obtain the final result.
