Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Fast Private Location-based Information Retrieval Over the Torus

Joon Soo Yoo, Mi Yeon Hong, Ji Won Heo, Kang Hoon Lee, Ji Won Yoon

TL;DR

This paper tackles privacy leakage in location-based services by introducing LocPIR, a TFHE-based private information retrieval framework that operates on encrypted GPS data to securely retrieve data from the public cloud. The approach leverages non-polynomial evaluation capabilities of TFHE, notably a homomorphic comparison circuit, to determine whether coordinates lie within bounding boxes and to select corresponding services without exposing user location. Key contributions include efficient encoding/encryption, a robust HomCompS comparison gate, a LocPIR circuit for box-based retrieval, and a XOR-based aggregation with minimal client interaction; the method is validated on a COVID-19 alert model with performance around $O(N(m+l))$ and total times near $4.36$–$5.67$ seconds for typical parameters. The results indicate practical feasibility for privacy-preserving location queries on public clouds, enabled by TFHE’s bootstrapping efficiency and a highly parallelizable circuit design.

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.

Fast Private Location-based Information Retrieval Over the Torus

TL;DR

This paper tackles privacy leakage in location-based services by introducing LocPIR, a TFHE-based private information retrieval framework that operates on encrypted GPS data to securely retrieve data from the public cloud. The approach leverages non-polynomial evaluation capabilities of TFHE, notably a homomorphic comparison circuit, to determine whether coordinates lie within bounding boxes and to select corresponding services without exposing user location. Key contributions include efficient encoding/encryption, a robust HomCompS comparison gate, a LocPIR circuit for box-based retrieval, and a XOR-based aggregation with minimal client interaction; the method is validated on a COVID-19 alert model with performance around and total times near – seconds for typical parameters. The results indicate practical feasibility for privacy-preserving location queries on public clouds, enabled by TFHE’s bootstrapping efficiency and a highly parallelizable circuit design.

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.
Paper Structure (17 sections, 5 figures, 2 tables, 5 algorithms)

This paper contains 17 sections, 5 figures, 2 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Contribution
  3. Related Work
  4. Background
  5. Notation
  6. Homomorphic Encryption
  7. Our Model
  8. LocPIR
  9. Encoding and Encryption
  10. Comparison Gate Design
  11. LocPIR Circuit
  12. Services Summation: HomAddXOR
  13. Experiment
  14. Result
  15. Privacy-preserving COVID-19 Alert Model
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Bounding Box and Boundary Coordinates for Seoul and Busan.
  • Figure 2: An Overview Timeline of Our Model.
  • Figure 3: Diagram Illustrating Comparison Gate Evaluation and Validation Step.
  • Figure 4: LocPIR's General Execution Time with Key Parameters: Number of Bounding Boxes $N$, Input Length $l$, and Service Length $m$.
  • Figure 5: Execution Time w.r.t Thread Count $n_t$ for Varying Numbers of Bounding Boxes $N=4, 8, 12, 16, 20, 24, 28$.