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February 8, 2026Cybersecurity0 citationsOpen Access

GPU-accelerated Batch Private Information Retrieval with lower communication overheads

YGYi-Tian GaoBZBowen ZhengYWYi Wang

Key Points

  • The aim is to improve the efficiency of Batch PIR schemes while reducing communication overheads.
  • Proposed a novel Oblivious Ciphertext Decompression scheme based on the Garbled Cuckoo Table algorithm.
  • Analyzed and optimized Spiral PIR using GPU technology.
  • Conducted evaluations of the Batch PIR protocol alongside its application in an anonymous messaging protocol.
  • Reduced additional communication costs in Batch PIR by 27%.
  • Achieved a 7.9 times improvement in efficiency compared to existing methods.
  • Realized a 267 times speedup in processing with GPU and a 3.9 times reduction in total computational costs.
  • Reduced latency by 18.8% in an anonymous messaging protocol with a substantial number of records.

Abstract

Abstract Private Information Retrieval (PIR) is a critical component in many privacy-preserving systems, and Batch PIR schemes constructed by Probabilistic Batch Code have garnered widespread attention in both academia and industry due to their relatively low average computational cost. However, existing Batch PIR still face challenges in balancing computational and communication efficiency, while some also exhibit poor adaptability to databases of large entries. In this paper, building upon the state-of-the-art Batch PIR schemes, we employ two approaches to enhance their overall performance. To reduce the communication cost of Batch PIR, we propose a novel Oblivious Ciphertext Decompression scheme GCTObvDecomperss GCTObvDecomperss based on the 3-Hash Garbled Cuckoo Table algorithm. We use the hypergraph peeling algorithm to construct this scheme and give a formal security definition and proof of this scheme to ensure it is computationally oblivious. In the implementation, our scheme reduces the additional communication cost in Batch PIR by 27% while achieving a 7. 9 × improvement in efficiency compared to existing solutions. Moreover, to enhance computational performance, we analyze the computational bottleneck of Spiral PIR and optimize it using GPU technology, achieving a 267 × speedup in the first-dimensional processing phase compared to serial execution, and reducing the total computation cost by 3. 9 ×. Furthermore, we conduct comprehensive evaluations of our Batch PIR protocol, demonstrating its superior performance in both computational efficiency and communication overhead, as well as its capability to support efficient retrieval in databases of large entries. Finally, we use our Batch PIR in an anonymous messaging protocol Pung, and evaluate the performance of this real-world application. By using our Batch PIR, the latency can be reduced by 18. 8% when the number of records reached 2^18 2 18 compared to the original protocol, indicating that our construction can provide a more effective and practical method in related scenarios.

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Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/698827c90fc35cd7a8846ccchttps://doi.org/10.1186/s42400-025-00418-w
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