On Secure Distributed Linearly Separable Computation

TL;DR

This paper introduces a secure framework for distributed linearly separable computation, ensuring data privacy without increasing communication costs, and proposes schemes that outperform existing data assignment methods.

Contribution

The authors develop a secure computation scheme that maintains optimal communication cost while minimizing randomness, outperforming existing data assignment strategies.

Findings

Any non-secure scheme can be made secure without extra communication cost.

Proposed a novel data assignment scheme that outperforms fractional repetition and cyclic assignments.

Derived an information-theoretic lower bound on the randomness size.

Abstract

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a subset of input datasets to each server, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost. Then we focus on the case where the computation cost of each server is minimum and aim to minimize the size of the randomness variable introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment. We then propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.