Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
원문 초록
Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval (PIR) system designed to preserve user privacy while enabling efficient and scalable query processing. VeLoPIR introduces three operational modes-interval validation, coordinate validation, and identifier matching-that support a broad range of real-world applications, including information and emergency alerts. To enhance performance, VeLoPIR incorporates multi-level algorithmic optimizations with parallel structures, achieving significant scalability across both CPU and GPU platforms. We also provide formal security and privacy proofs, confirming the system's robustness under standard cryptographic assumptions. Extensive experiments on real-world datasets demonstrate that VeLoPIR achieves up to 11.55 times speed-up over a prior baseline. The implementation of VeLoPIR is publicly available at https://github.com/PrivStatBool/VeLoPIR.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Fast k-NN Graph Construction by GPU based NN-Descent
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration Models
FlashDMoE: Fast Distributed MoE in a Single Kernel
토론
이 논문과 연결된 포럼 글이 아직 없습니다.