Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus

TL;DR

VeLoPIR is a versatile, scalable location-based private information retrieval system that uses fully homomorphic encryption over the torus, supporting multiple operational modes and demonstrating significant performance improvements.

Contribution

The paper introduces VeLoPIR, a novel PIR system with multiple operational modes, optimized algorithms, and formal security proofs, advancing privacy-preserving location-based services.

Findings

Achieves up to 11.55x speed-up over previous methods

Supports diverse real-world applications including alerts

Demonstrates scalability on CPU and GPU platforms

Abstract

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.