Efficient Single-Server Private Information Retrieval Based on LWE Encryption

Hai Huang, Zhibo Guan, Bin Yu*, Xiang Li, Mengmeng Ge, Chao Ma, Xiangyu Ma

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

Private Information Retrieval (PIR) is a cryptographic protocol that allows users to retrieve data from one or more databases without revealing any information about their queries. Among existing PIR protocols, single-server schemes based on the Learning With Errors (LWE) assumption currently constitute the most practical class of constructions. However, existing schemes continue to suffer from high client-side preprocessing complexity and significant server-side storage overhead, leading to degraded overall performance. We propose ShufflePIR, a single-server protocol that marks the first introduction of an SM3-based pseudorandom function into the PIR framework for shuffling during preprocessing and utilizes cryptographic hardware to accelerate computation, thereby improving both efficiency and security. In addition, the adoption of a parallel encryption scheme based on the LWE assumption significantly enhances the client’s computational efficiency when processing long-bit data. We evaluate the performance of our protocol against the latest state-of-the-art PIR schemes. Simulation results demonstrate that ShufflePIR achieves a throughput of 9903 MB/s on a 16 GB database with 1 MB records, outperforming existing single-server PIR schemes. Overall, ShufflePIR provides an efficient and secure solution for privacy-preserving information retrieval in a wide range of applications. © 2025 by the authors.
Original languageEnglish
Article number3373
Number of pages20
JournalMathematics
Volume13
Issue number21
Online published23 Oct 2025
DOIs
Publication statusPublished - Nov 2025

Funding

This research was funded by the Key Research and Development Program of Heilongjiang Province (2022ZX01A36), Harbin Manufacturing Science and Technology Innovation Talent Project (2022CXRCCG004), and the National Key Research and Development Plan Project (2023YFB4403500).

Research Keywords

  • private information retrieval
  • learning with errors
  • single server

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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