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

2025-06-15

AI Summary

Location-based services require users to disclose sensitive location data, yet untrusted servers may misuse such data, posing severe privacy risks. To address this, we propose VeLoPIR—a novel private information retrieval (PIR) framework supporting three modalities: range verification, coordinate validation, and identifier matching. VeLoPIR uniquely integrates ring-based fully homomorphic encryption (TFHE) with multi-level parallel optimization (CPU/GPU co-processing), achieving formal security guarantees while balancing generality, efficiency, and cross-platform scalability. Evaluated on real-world datasets, VeLoPIR achieves up to 11.55× speedup over state-of-the-art baselines. All system components are open-sourced, and its privacy robustness is rigorously proven under standard cryptographic assumptions.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Distributed Search
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Enables private location-based queries without exposing sensitive data
• Supports versatile applications like emergency alerts with privacy guarantees
• Achieves high-speed performance via CPU/GPU optimizations and parallel processing

Innovation

• Fully homomorphic encryption for private retrieval
• Multi-level optimizations with parallel structures
• Three operational modes for versatile applications