Splintering with distributions: A stochastic decoy scheme for private computation. (arXiv:2007.02719v3 [cs.LG] UPDATED)

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 …

arxiv stochastic