Fast Private Location-based Information Retrieval Over the Torus

TL;DR

LocPIR is a privacy-preserving location-based information retrieval framework using homomorphic encryption, enabling secure data access from public clouds with minimal interaction and high efficiency.

Contribution

It introduces a novel homomorphic encryption-based framework, LocPIR, leveraging TFHE for efficient, private location data retrieval with low overhead.

Findings

Demonstrates practical computational speed for LocPIR.

Shows reduced client-server interaction and memory overhead.

Validates effectiveness through a COVID-19 alert application.

Abstract

Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE's expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud.