Private Information Retrieval from Storage Constrained Databases -- Coded Caching meets PIR

TL;DR

This paper introduces a novel PIR scheme for storage-constrained databases that optimally balances storage and download cost, extending PIR capabilities beyond fully replicated databases.

Contribution

It proposes a general achievable scheme for PIR with any normalized storage level, unifying and improving upon existing PIR solutions for constrained storage.

Findings

Achieves optimal download cost for full storage case (t=N).

Outperforms memory-sharing strategies for intermediate storage levels.

Provides a unified scheme applicable to any storage constraint.

Abstract

Private information retrieval (PIR) allows a user to retrieve a desired message out of $K$ possible messages from $N$ databases without revealing the identity of the desired message. Majority of existing works on PIR assume the presence of replicated databases, each storing all the $K$ messages. In this work, we consider the problem of PIR from storage constrained databases. Each database has a storage capacity of $\mu KL$ bits, where $K$ is the number of messages, $L$ is the size of each message in bits, and $\mu \in [1/N, 1]$ is the normalized storage. In the storage constrained PIR problem, there are two key design questions: a) how to store content across each database under storage constraints; and b) construction of schemes that allow efficient PIR through storage constrained databases. The main contribution of this work is a general achievable scheme for PIR from storage constrained databases for any value of storage. In particular, for any $(N,K)$, with normalized storage $\mu= t/N$, where the parameter $t$ can take integer values $t \in \{1, 2, \ldots, N\}$, we show that our proposed PIR scheme achieves a download cost of $\left(1+ \frac{1}{t}+ \frac{1}{t^{2}}+ \cdots + \frac{1}{t^{K-1}}\right)$. The extreme case when $\mu=1$ (i.e., $t=N$) corresponds to the setting of replicated databases with full storage. For this extremal setting, our scheme recovers the information-theoretically optimal download cost characterized by Sun and Jafar as $\left(1+ \frac{1}{N}+ \cdots + \frac{1}{N^{K-1}}\right)$. For the other extreme, when $\mu= 1/N$ (i.e., $t=1$), the proposed scheme achieves a download cost of $K$. The interesting aspect of the result is that for intermediate values of storage, i.e., $1/N < \mu <1$, the proposed scheme can strictly outperform memory-sharing between extreme values of storage.