Private Read Update Write (PRUW) with Storage Constrained Databases

TL;DR

This paper addresses private read-update-write operations in storage-constrained databases, proposing a storage scheme that minimizes communication costs in federated submodel learning scenarios.

Contribution

It introduces a novel storage mechanism tailored for PRUW with storage constraints, outperforming coded and divided storage methods in minimizing communication costs.

Findings

Proposed storage scheme reduces total communication cost.

Achieves lower cost than coded or divided storage approaches.

Optimizes storage content placement before PRUW operations.

Abstract

We investigate the problem of private read update write (PRUW) in relation to federated submodel learning (FSL) with storage constrained databases. In PRUW, a user privately reads a submodel from a system of $N$ databases containing $M$ submodels, updates it locally, and writes the update back to the databases without revealing the submodel index or the value of the update. The databases considered in this problem are only allowed to store a given amount of information specified by an arbitrary storage constraint. We provide a storage mechanism that determines the contents of each database prior to the application of the PRUW scheme, such that the total communication cost is minimized. We show that the proposed storage scheme achieves a lower total cost compared to what is achieved by using \emph{coded storage} or \emph{divided storage} to meet the given storage constraint.