IM-PIR: In-Memory Private Information Retrieval

Abstract

Private information retrieval (PIR) is a cryptographic primitive that allows a client to securely query one or multiple servers without revealing their specific interests. In spite of their strong security guarantees, current PIR constructions are computationally costly. Specifically, most PIR implementations are memory-bound due to the need to scan extensive databases (in the order of GB), making them inherently constrained by the limited memory bandwidth in traditional processor-centric computing architectures. Processing-in-memory (PIM) is an emerging computing paradigm that augments memory with compute capabilities, addressing the memory bandwidth bottleneck while simultaneously providing extensive parallelism. Recent research has demonstrated PIM's potential to significantly improve performance across a range of data-intensive workloads, including graph processing, genome analysis, and machine learning. In this work, we propose the first PIM-based architecture for multi-server PIR. We discuss the algorithmic foundations of the latter and show how its operations align with the core strengths of PIM architectures: extensive parallelism and high memory bandwidth. Based on this observation, we design and implement IM-PIR, a PIM-based multi-server PIR approach on top of UPMEM PIM, the first openly commercialized PIM architecture. Our evaluation demonstrates that a PIM-based multi-server PIR implementation significantly improves query throughput by more than 3.7x when compared to a standard CPU-based PIR approach.