Splintering with distributions: A stochastic decoy scheme for private computation

Understand

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.