Private Edge Computing for Linear Inference Based on Secret Sharing

TL;DR

This paper introduces a privacy-preserving edge computing scheme for linear inference that uses secret sharing and partial replication to ensure data privacy, reduce latency, and handle server stragglers.

Contribution

It proposes a novel secret sharing-based scheme that guarantees information-theoretic privacy while optimizing latency and mitigating straggler effects in edge computing.

Findings

Guarantees information-theoretic privacy against eavesdroppers.

Reduces overall latency through joint beamforming in download phase.

Mitigates impact of straggling servers with partial replication.

Abstract

We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge servers located at the edge of the network, but do not want the servers, or any other party with access to the wireless links, to gain any information about their data. We provide a scheme that guarantees information-theoretic user data privacy against an eavesdropper with access to a number of edge servers or their corresponding communication links. The novelty of the proposed scheme lies in the utilization of secret sharing and partial replication to provide privacy, mitigate the effect of straggling servers, and to allow for joint beamforming opportunities in the download phase, to minimize the overall latency, consisting of upload, computation, and download latencies.