EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

Abstract

This research focuses on developing EPLQ (Efficient Privacy-Preserving Location-Based Query) system, addressing the critical need for secure and efficient location-based queries on outsourced data. The system leverages advanced cryptographic techniques to protect user privacy while enabling efficient query processing. We propose a novel approach that minimizes communication overhead and computation cost compared to existing methods. Evaluation demonstrates significant improvements in both efficiency and privacy guarantees, enabling secure and scalable location-based services in various applications.

Introduction

Location-based services (LBS) are ubiquitous, but often compromise user privacy due to the sensitive nature of location data. Outsourcing data to cloud servers offers scalability and cost benefits, but raises concerns about data security and privacy breaches. Existing solutions for privacy-preserving location queries often suffer from high computational complexity, communication overhead, or limited functionality. This research aims to bridge this gap by developing an efficient and privacy-preserving system for handling location-based queries over encrypted data stored on a remote server, mitigating privacy risks while maintaining query performance.

Objectives

• Develop an efficient encryption scheme for location data that minimizes computational overhead.
• Design an optimized query processing algorithm for encrypted data on the cloud server.
• Implement a secure and efficient system that guarantees user privacy while maintaining query performance.

Technical Details

• Advanced encryption techniques (e.g., Order-Preserving Encryption, Homomorphic Encryption)
• Secure query processing over encrypted data
• Location data indexing and secure search schemes
• Tech Stack: Python, Flask/Django, PostgreSQL/MySQL, Cryptography Libraries
• Deployment on simulated cloud environments (AWS/GCP/Local)