H-LPS: a hybrid approach for user's location privacy in location-based services

TL;DR

H-LPS is a hybrid location privacy scheme combining obfuscation and collaboration to enhance user privacy in location-based services while maintaining acceptable service accuracy.

Contribution

The paper introduces H-LPS, a novel hybrid approach that improves location privacy using a combination of obfuscation and collaboration techniques.

Findings

H-LPS achieves higher privacy levels than existing schemes.

H-LPS maintains good service accuracy for most users.

Compared to state-of-the-art, H-LPS offers a balanced privacy-accuracy trade-off.

Abstract

Applications providing location-based services (LBS) have gained much attention and importance with the notion of the internet of things (IoT). Users are utilizing LBS by providing their location information to third-party service providers. However, location data is very sensitive that can reveal user's private life to adversaries. The passive and pervasive data collection in IoT upsurges serious issues of location privacy. Privacy-preserving location-based services are a hot research topic. Many anonymization and obfuscation techniques have been proposed to overcome location privacy issues. In this paper, we have proposed a hybrid location privacy scheme (H-LPS), a hybrid scheme mainly based on obfuscation and collaboration for protecting users' location privacy while using location-based services. Obfuscation naturally degrades the quality of service but provides more privacy as compared to anonymization. Our proposed scheme, H-LPS, provides a very high-level of privacy yet provides good accuracy for most of the users. The privacy level and service accuracy of H-LPS are compared with state-of-the-art location privacy schemes and it is shown that H-LPS could be a candidate solution for preserving user location privacy in location-based services.