Privacy-Preserving Coding Schemes for Multi-Access Distributed Computing Models

TL;DR

This paper introduces privacy-preserving coding schemes for multi-access distributed computing models, enhancing data privacy while reducing communication bottlenecks in large-scale distributed frameworks.

Contribution

It develops new private coded schemes for MADC models by constructing extended placement delivery arrays that ensure privacy of reducer functions.

Findings

New private coding schemes for MADC models

Construction of extended placement delivery arrays

Guarantee of privacy for reducer functions

Abstract

Distributed computing frameworks such as MapReduce have become essential for large-scale data processing by decomposing tasks across multiple nodes. The multi-access distributed computing (MADC) model further advances this paradigm by decoupling mapper and reducer roles: dedicated mapper nodes store data and compute intermediate values, while reducer nodes are connected to multiple mappers and aggregate results to compute final outputs. This separation reduces communication bottlenecks without requiring file replication. In this paper, we introduce privacy constraints into MADC and develop private coded schemes for two specific connectivity models. We construct new families of extended placement delivery arrays and derive corresponding coding schemes that guarantee privacy of each reducer's assigned function.