Achieving High Privacy Protection in Location-based Recommendation Systems

Tahani Alnazzawi,Nermin Hamza,Reem Alotaibi

doi:10.14569/ijacsa.2019.0101027

Abstract

In recent years, privacy has become great attention in the research community. In Location-based Recommendation Systems (LbRSs), the user is constrained to build queries depend on his actual position to search for the closest points of interest (POIs). An external attacker can analyze the sent queries or track the actual position of the LbRS user to reveal his\her personal information. Consequently, ensuring high privacy protection (which is including location privacy and query privacy) is a fundamental thing. In this paper, we propose a model that guarantees high privacy protection for LbRS users. The model is work by three components: The first component (selector) uses a new location privacy protection approach, namely, the smart dummy selection (SDS) approach. The SDS approach generates a strong dummy position that has high resistance versus a semantic position attack. The second component (encryptor) uses an encryption-based approach that guarantees a high level of query privacy versus a sampling query attack. The last component (constructor) constructs the protected query that is sent to the LbRS server. Our proposed model is supported by a checkpoint technique to ensure a high availability quality attribute. Our proposed model yields competitive results compared to similar models under various privacy and performance metrics.

Highlights

The expression data mining indicates to software tools and mathematical modeling techniques which are applied to detection patterns in data and used to build models [1]
The valuable advantages of Location-based Recommendation Systems (LbRSs) are accompanied by risks since the users are forced to reveal their real locations, which can be exploited by attackers to attack the privacy of the LbRS users
In this aspect of the research field, we contribute by proposing a smart dummy selection (SDS) approach for preserving the location privacy of the LbRS users

Summary

Introduction

The expression data mining indicates to software tools and mathematical modeling techniques which are applied to detection patterns in data and used to build models [1]. Recommender systems were categorized into three major types, including Collaborative Filtering (CF), Content-Based (CB) and Hybrid [3]. Later on, combining these based recommender types; novel recommender system types were introduced where location-aware systems are becoming more widespread due to massive usage of smart devices. In Location-based Recommender Systems (LbRSs), the user requires recommendations for his/her Points of Interest (POIs).

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