Private Sequential Function Computation

08/03/2019 ∙ by Behrooz Tahmasebi, et al. ∙ 0

In this paper, we introduce the problem of private sequential function computation, where a user wishes to compute a composition of a sequence of K linear functions, in a specific order, for an arbitrary input. The user does not run these computations locally, rather it exploits the existence of N non-colluding servers, each can compute any of the K functions on any given input. However, the user does not want to reveal any information about the desired order of computations to the servers. For this problem, we study the capacity C, defined as the supremum of the number of desired computations, normalized by the number of computations done at the servers, subject to the privacy constraint. In particular, we prove that (1-1/N)/ (1-1/(K,N)) < C < 1. For the achievability, we show that the user can retrieve the desired order of computations, by choosing a proper order of inquiries among different servers, while keeping the order of computations for each server fixed, irrespective of the desired order of computations. In the end, we develop an information-theoretic converse which results an upper bound on the capacity.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

page 11

page 16

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.