HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

Abstract

Nowadays, location-based services are being widely popularized due to their massive usage in current and emerging technologies. These services are based on searching out areas of interest which are likely to be accessed by users. Despite helping users worldwide, Location Based Services (LBSs) Systems endanger users' privacy because a user must provide personal information in order to use the services. Users thus become easy prey for assailants to access their social and personal lives. This problem is a giant issue for contemporary technologies because they are increasingly being used with the passage of time. Many existing solutions have attempted to resolve the challenges, but they face some serious dilemmas regarding the preservation of privacy. In order to address the privacy challenges in LBS systems, in this paper we have introduced a new Hierarchy Based Location Privacy (HBLP) model that protects the user's privacy, including the user's query time and identity and location information. The proposed model protects the user's privacy by using pseudo identity exchange, an aggregation protocol, and the concepts of Forest User (FU), Tree User (TU), and Child Users (CU) with k-anonymity and t-closeness, which is a reasonable combination for privacy provision for a user's query time, identity, and location. In order to evaluate the privacy protection level, we implemented the HBLP model in a Riverbed (Opnet) simulation and compared the results with existing state-of-the-art privacy-provisioning methods. The results showed that HBLP protected all the privacy attributes when a user interacts with an LBS system. INDEX TERMS Location based services, user privacy, NTTP, point of interest.

An Advanced Dummy Position-Based Privacy Provisioning Framework for TTP-Based LBS System

“…However, location cloaking is accessible to query tracking attacks, where the opponent can determine the querier by comparing the two sectors in the LBS queries. Similarly, Alsubhi et al [39] proposed HBLP privacy protection scheme using PID and PLAM protocols in a peer to peer (P2) model. HBLP improved overall privacy while a user interacts with LBS system but all the query content was depended on forest user.…”

“…To determine the privacy level, we have evaluated the anonymous entropy and compared the proposed PDAS with three existing different dummy position-based approaches, including E-DLS algorithm [37], random dummy positionbased algorithm, Dest-ex method [38], and HBLP [39]. In Figure . 13, we noticed that ADPA attained the maximum entropy level and reached up to 3.68 when the number of dummy positions (K) is 10.…”

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