Capacity-Achieving Private Information Retrieval Schemes from Uncoded Storage Constrained Servers with Low Sub-packetization
Abstract
This paper investigates reducing sub-packetization of capacity-achieving schemes for uncoded Storage Constrained Private Information Retrieval (SC-PIR) systems. In the SC-PIR system, a user aims to retrieve one out of files from servers while revealing nothing about its identity to any individual server, in which the files are stored at the servers in an uncoded form and each server can store up to equivalent files, where is the normalized storage capacity of each server. We first prove that there exists a capacity-achieving SC-PIR scheme for a given storage design if and only if all the packets are stored exactly at servers for such that . Then, the optimal sub-packetization for capacity-achieving linear SC-PIR schemes is characterized as the solution to an optimization problem, which is typically hard to solve because of involving indicator functions. Moreover, a new notion of array called Storage Design Array (SDA) is introduced for the SC-PIR system. With any given SDA, an associated capacity-achieving SC-PIR scheme is constructed. Next, the SC-PIR schemes that have equal-size packets are investigated. Furthermore, the optimal equal-size sub-packetization among all capacity-achieving linear SC-PIR schemes characterized by Woolsey et al. is proved to be . Finally, by allowing unequal size of packets, a greedy SDA construction is proposed, where the sub-packetization of the associated SC-PIR scheme is upper bounded by . Among all capacity-achieving linear SC-PIR schemes, the sub-packetization is optimal when or , and within a multiplicative gap of the optimal one otherwise. In particular, for the case where , another SDA is constructed to obtain lower sub-packetization.
Keywords
Cite
@article{arxiv.2102.08058,
title = {Capacity-Achieving Private Information Retrieval Schemes from Uncoded Storage Constrained Servers with Low Sub-packetization},
author = {Jinbao Zhu and Qifa Yan and Xiaohu Tang and Ying Miao},
journal= {arXiv preprint arXiv:2102.08058},
year = {2021}
}