VIPIR: A Versatile GPU Framework for Integrating Private Information Retrieval Protocols

• URL: http://arxiv.org/abs/2606.11536v1
• Date: Wed, 10 Jun 2026 00:40:14 GMT
• Title: VIPIR: A Versatile GPU Framework for Integrating Private Information Retrieval Protocols
• Abstract summary: VIPIR is a versatile GPU framework that co-designs PIR protocols with GPU acceleration.<n>We develop a unified analytic model showing that state-of-the-art PIR protocols fall into two categories with complementary limitations.<n>We propose two protocols that flexibly combine techniques across these categories, overcoming the limitations of both classes.
• Score: 13.257148302181086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While private information retrieval (PIR) enables private database services by fully concealing access patterns, it simultaneously requires high computational throughput, large memory capacity, and substantial memory bandwidth. We introduce VIPIR, a versatile GPU framework that co-designs PIR protocols with GPU acceleration. We develop a unified analytic model showing that state-of-the-art PIR protocols fall into two categories with complementary limitations, and propose two protocols that flexibly combine techniques across these categories, overcoming the limitations of both classes. These protocols incorporate a GPU-friendly data compression method called expansion-based ring packing (ExpPack), which offers a high degree of parallelism and minimal communication cost. VIPIR applies further optimizations to core operations, including number-theoretic transforms (NTTs) and various matrix-matrix multiplications (GEMMs). Notably, we develop a tensor-core-based execution method for database multiplication by interpreting it as a mixed-integer-type GEMM. We also design memory-efficient scheduling methods that minimize intermediate buffers and enable multi-GPU scaling under memory capacity constraints. Overall, VIPIR achieves orders-of-magnitude higher throughput than prior PIR systems while reducing communication and memory overheads, making large-scale PIR practical.

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