GPU-Accelerated DPF-Based Private Information Retrieval for Large-Scale Database

作者：Qingquan Tan, Chenkai Zeng, Qi Feng, Cong Peng, Weijia Wang, Debiao He · 发表于：IACR Transactions on Cryptographic Hardware and Embedded Systems · 年份：2026 · DOI：10.46586/tches.v2026.i3.933-957 · 研究领域：Cryptography and Data Security、Advanced Database Systems and Queries、Data Management and Algorithms

Private Information Retrieval (PIR) enables a client to retrieve a database record without revealing the queried index. Among existing constructions, DPFbased PIR (DPF-PIR) is attractive due to its low communication cost and ease of deployment. However, its server-side computation remains a bottleneck because both computation and memory access scale linearly with the database size. In this work, we present a GPU-accelerated design for DPF-PIR that significantly improves the efficiency of its two dominant components: vector generation and inner product evaluation. For vector generation, we analyze the binary tree expansion of DPF evaluation and develop a GPU-oriented execution strategy that reduces redundant memory accesses and alleviates memory conflicts. For inner product evaluation, we identify that database access dominates runtime and follows a probabilistic access pattern. Based on this observation, we propose a memory-efficient distribution-aware access optimization with thread-level access coalescing, significantly reducing memory traffic. We further leverage CUDA primitives for efficient reduction and optimize batch processing by improving GPU cache utilization.We conduct extensive experiments on two distinct implementations: 1) non-pipelined DPF-PIR, which trades higher memory for low latency, and 2) pipelined DPF-PIR, which reduces memory usage and supports larger batch sizes. Across database sizes from 219 to 224, our non-pipelined PIR improves throughput by 8.20–9...