An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Hasan, A S M Touhidul; Qu, Qiang; Li, Chengming; Chen, Lifei; Jiang, Qingshan

doi:10.3390/ijgi7020053

Cited by 20 publications

(12 citation statements)

References 37 publications

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“…None of the existing privacy approaches for LBS systems can anonymize the mobile user trajectories while keeping a small energy footprint to the best of our knowledge. For instance, Hasan et al 6 proposed a privacy architecture to preserve user trajectories in reward‐based LBS applications that can anonymize the user trajectories with a fixed global location. However, the proposed approach suffers from a significant processing overhead when it is used in real‐world applications.…”

Section: Related Workmentioning

confidence: 99%

“…One of the major concerns of any LBS‐based system is the privacy of mobile user trajectories. Most of the existing LBS approaches 5,6 collect user locations over time with user identities periodically. This could lead to different kinds of personal privacy breaches if a leak of the identified trajectories occurs.…”

Section: Introductionmentioning

confidence: 99%

“…To address these challenges, we developed a bounded perturbation technique to anonymize user's trajectories against any privacy breaches to address these challenges. The developed privacy mechanism prevents our LBS‐based system from generating any visiting trajectories in real‐time using the Global Location Set (GLS) 6 strategy.…”

Section: Introductionmentioning

confidence: 99%

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A privacy‐preserving mobile location‐based advertising system for small businesses

Ahmed

2021

Engineering Reports

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Small‐to‐medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location‐Based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy‐preserving system, which has parts executing on smartphones as a mobile app, as well as a web‐based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client‐server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi‐modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine model. The multi‐modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo‐location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A privacy‐preserving mobile location‐based advertising system for small businesses

Ahmed

2021

Engineering Reports

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“…So, this paper was developed using the concept of technology acceptance behavior, which was then applied to LBS users to specify LBS usage intention, as well as technology acceptance behavior opinions. Hasan et al [49] preset the problem of personal privacy in LBS, proposed a privacy architecture, and focused the bounded perturbation technique on keeping the trajectory of each user from the privacy gaps.…”

Section: Related Workmentioning

confidence: 99%

BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

Albelaihy

Thayananthan

2019

Sensors

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Location-based services present an inherent challenge of finding the delicate balance between efficiency when answering queries and maintaining user privacy. Inevitable security issues arise as the server needs to be informed of the query location to provide accurate responses. Despite the many advancements in localization security in wireless sensor networks, servers can still be infected with malicious software. It is now possible to ensure queries do not generate any fake responses that may appear real to users. When a fake response is used, there are mechanisms that can be employed so that the user can identify the authenticity of the query. For this reason, this paper proposes Bloom Filter 0 Knowledge (BL0K), which is novel phase privacy method that preserves the framework for location-based service (LBS) and combines a Bloom filter and the Zero knowledge protocol. The usefulness of these methods has been shown for securing private user information. Analysis of the results demonstrated that BL0K performance is decidedly better when compared to the referenced approaches using the privacy entropy metric.

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“…A plethora of studies have been conducted for handling mobile objects' trajectory data. More precisely, several of them attempt to reduce the storage size [3][4][5], while others investigate the privacy preservation of trajectory data [6,7]. Nowadays, not only are storage-efficient spatio-temporal transformation schemes needed, but also secure querying on large-scale spatio-temporal data [8].…”

mentioning

confidence: 99%

Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

et al. 2019

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The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1. To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work. established as trajectory or mobility mining [1]. Also, the technology of databases is evolving to support the querying and representation of the trajectory of moving objects (e.g., humans, animals, vehicles, natural phenomena). Hence, the main parts of trajectory data-mining include pre-processing, data management, query processing, trajectory data-mining tasks, and privacy protection [2].Real-life applications, such as the analysis of traffic congestion, intelligent transportation, animal immigration habits analysis, cellular communications, military applications, structural and environmental monitoring, disaster/rescue management, as well as remediation, Geographic Information Systems (GIS), Location-Based Services (LBS), and other domains have increased the interest in the area of trajectory data-mining and efficient management of spatio-temporal data.It should be noted that the explosive growth of social media has produced large-scale mobility datasets whose publication puts people's personal lives at severe risk. Indeed, users get used to sharing their most-visited or potentially sensitive locations, such as their home, workplace, and holiday locations that are easy to obtain through social media. No...

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An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Cited by 20 publications

References 37 publications

A privacy‐preserving mobile location‐based advertising system for small businesses

A privacy‐preserving mobile location‐based advertising system for small businesses

BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

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