Use Of Location-based Services Research Articles

Location Based Privacy Preserving using Machine Learning

Location-based privacy preservation is crucial in the digital age due to the widespread use of location-based services and growing concerns about individual privacy. Despite the use of k-anonymity measures, current systems face challenges, particularly the risk of re-identification, especially when attackers have additional contextual information. These systems also suffer from information loss, leading to a significant decrease in data utility. Striking the right balance between privacy and data utility remains a prominent challenge in the field of location-based privacy preservation. To address the existing gaps and challenges in privacy-preserving location data publishing, a robust framework termed Location anonymization framework is being introduced which consists of three methods Generalization, Sequence alignment and Clustering. DGH Trees are being used to minimize the information loss and also maintains a balance between privacy and utility. To enhance privacy preservation in overly sensitive datasets, a modified version of the k-means algorithm is being put forth. Moreover, to improve the alignment process, the more efficient iterative multisequence alignment is being opted for over the progressive counterpart within this framework. The aim is to achieve a more balanced trade-off between privacy and utility in location data publishing.

Aplikasi Pencarian Tempat Nongkrong Daerah Kebayoran Lama dengan Metode LBS (Location-Based Service) menggunakan Android Studio

The purpose of this study is to design an Android-based application for finding hangout places in the Kebayoran Lama area using the LBS (Location-based service) method using Android Studio due to the lack of public knowledge about hangout places spread across the Kebayoran Lama area, South Jakarta, so they have to know the name of the Hangout Place. first if you want to search on google maps and ask the local community so it's not time efficient. This application can be run on the Android platform as a means of interface with the user because of the ease and efficiency it has. Besides that, using Android devices is common and has even become a necessity for most people today. During the data collection process, observations were made at the research location involving interviews with the community around Kebayoran Lama, South Jakarta, DKI-Jakarta. At the application development stage using the XP (Extreme Programming) method and the use of Location-Based Service (LBS) as location services and mobile devices. The results of the research in the form of an application to find hangout places in Kebayoran Lama, South Jakarta using the Location-Based Service (LBS) method are expected to help people in Kebayoran Lama, South Jakarta to find locations where to hang out that provide cheap food and drinks, so that it can make it easier for people to use their time efficiently.

A Review on Energy Optimization in Location-Based Services for 5G and IoT Networks

Fifth generation mobile technology (5G) targets to meet user demands in terms of improved capability, increased data rate, and best value of service. In order to meet these demands, the 5G technology applies massive multiple-input multiple-output (MIMO) and device-to-device communication (D2D). Also, by making use of location-based services (LBS), it provides value-added services to the users including methods to overcome the energy depletion in the network. The authors discuss the internet of things and its potential to offer novel solutions. Media access control (MAC) sub-layer offers and channel access control mechanisms will enable various terminals or network nodes to interact in a network and prevent collisions. Here, the authors present a complete review related to developing and enabling technologies with the key focus on 5G mobile networks and supporting the growth in traffic for enabling IoT.

Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

AbstractWith the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer calledPPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geoprivacy of active Twitter users who frequently share geotagged tweets in their home and work locations. By analyzing over 38,000 geotagged tweets of 93 active Twitter users in three U.S. cities, the two-dimensional Gaussian masking technique with proper standard deviation settings is found to be more effective to protect user's location privacy while sacrificing geospatial analytical resolution than the random perturbation masking method and the aggregation on traffic analysis zones. Furthermore, a three-dimensional theoretical framework considering privacy, analytics, and uncertainty factors simultaneously is proposed to assess geomasking techniques. Our research offers insights into geoprivacy concerns of social media users' georeferenced data sharing for future development of location-based applications and services.

Geographical Design: Spatial Cognition and Geographical Information Science

With spatial technologies ranging from mapping software to the use of location-based services, spatial knowledge is often acquired and communicated through geographic information technologies. This bo

Factors affecting customers’ intention to use of location-based services (LBS) in the lodging industry

PurposeThe purpose of this study is to investigate the determinants of customers’ intentions to use location-based services (LBS) offered by a hotel. The study examined whether hotel customers’ coupon proneness, trust, privacy concerns and familiarity with LBS are significant determinants of their intentions to use LBS.Design/methodology/approachAn online survey using a scenario-based narrative was administered to collect data from participants who have smartphones and have stayed at a full-service hotel within the previous 12 months. A research model tested data collected from 402 hotel customers, using confirmatory factor analysis and structural equation modeling.FindingsThree proposed determinants (i.e. familiarity, coupon proneness and trust) positively influenced customers’ intentions to use LBS. Out of the four dimensions of privacy concerns (concerns of collection, error, unauthorized secondary use and improper access), only concerns about data collection negatively influenced customers’ intentions to use a hotel’s LBS.Originality/valueThis study extends the literature on LBS adoption and other technology with privacy issues by modifying existing models and empirically testing it in the new context of hotels.

Efficient Utility Improvement for Location Privacy

Abstract The continuously increasing use of location-based services poses an important threat to the privacy of users. A natural defense is to employ an obfuscation mechanism, such as those providing geo-indistinguishability, a framework for obtaining formal privacy guarantees that has become popular in recent years. Ideally, one would like to employ an optimal obfuscation mechanism, providing the best utility among those satisfying the required privacy level. In theory optimal mechanisms can be constructed via linear programming. In practice, however, this is only feasible for a radically small number of locations. As a consequence, all known applications of geo-indistinguishability simply use noise drawn from a planar Laplace distribution. In this work, we study methods for substantially improving the utility of location obfuscation, while maintaining practical applicability as a main goal. We provide such solutions for both infinite (continuous or discrete) as well as large but finite domains of locations, using a Bayesian remapping procedure as a key ingredient. We evaluate our techniques in two real world complete datasets, without any restriction on the evaluation area, and show important utility improvements with respect to the standard planar Laplace approach.

Applications of Location-Based Services and Mobile Technologies in K-12 Classrooms

The use of location-based services and mobile technologies is increasing in K-12 classrooms. In this article, we describe the history and the current use of these tools in the innovative Geospatial Semester project in Virginia. We share a number of examples where students are creating projects of their own interest that use editable feature services, mobile data collection and other cutting-edge technologies. These projects help students build their spatial thinking and problem-solving skills, and help teachers build conceptual understanding in a variety of domains.

Security awareness and the use of location-based services, technologies and games

Rapid expansion and development in the modern mobile technology market has created an opportunity for the use of location-based technologies and games. Because of this fast expanding market and new technology, it is important to be aware of the implications this expansive technology could have on computer security. This paper will endeavour to measure the impact of location-based technologies and games on the security awareness of first- to fourth-year computer science university students. A questionnaire, posted on the web, and completed by computer science students from different year groups, was used to collect the data for this study. The major results of this study are the following: there is a difference in the security awareness of students who use and play location-based services, technologies and games and those who do not. This study also determined that the computer science students are cautious of security implications although they do not take preventative measures.

Security awareness and the use of location-based services, technologies and games

Security awareness and the use of location-based services, technologies and games

A Novel Dummy-Based KNN Query Anonymization Method in Mobile Services

Due to the advances of mobile devices with GPS (Global Positioning System), a user's privacy threat is increased in location based services (LBSs).So, various Location Privacy-Preserving Mechanisms (LPPMs) have been proposed in the literature to address the privacy risks derived from the exposure of user locations through the use of LBSs. However, these methods obfuscate the locations disclosed to the LBS provider using a variety of strategies, most of which come at a cost of resource consumption. Therefore, we propose a privacy-protected KNN query anonymization method based on Bayesian estimation for Location-based services. Unlike previous dummy-based approaches, in our method, the request to the LBS server doesn't contain the genuine user location, so we can't calculate whether meet the threshold condition of two location directly, but must to decision making by transition probability. In addition, our method just requires the server returns the results the client needs. Further, we propose an effective search algorithm to improve the server processing. So it can reduce bandwidth usages and efficiently support K-nearest neighbor queries without revealing the private information of the query issuer. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs. Keyword: Location privacy protection, Location-based services(LBSs),K-nearest neighbor query, Bayesian estimation

A Framework for Measuring Query Privacy in Location-based Service

The widespread use of location-based services (LBSs), which allows untrusted service provider to collect large number of user request records, leads to serious privacy concerns. In response to these issues, a number of LBS privacy protection mechanisms (LPPMs) have been recently proposed. However, the evaluation of these LPPMs usually disregards the background knowledge that the adversary may possess about users’ contextual information, which runs the risk of wrongly evaluating users’ query privacy. In this paper, we address these issues by proposing a generic formal quantification framework,which comprehensively contemplate the various elements that influence the query privacy of users and explicitly states the knowledge that an adversary might have in the context of query privacy. Moreover, a way to model the adversary’s attack on query privacy is proposed, which allows us to show the insufficiency of the existing query privacy metrics, e.g., k-anonymity. Thus we propose two new metrics: entropy anonymity and mutual information anonymity. Lastly, we run a set of experiments on datasets generated by network based generator of moving objects proposed by Thomas Brinkhoff. The results show the effectiveness and efficient of our framework to measure the LPPM.

위치기반서비스 이용에 대한 인지된 위험의 영향 연구

개인정보 관련 사건사고가 증가하면서 개인정보 기반의 모바일 서비스에 대한 우려가 늘고 있다. 본 연구는 스마트폰 보급 확대로 이용이 증가하고 있는 위치기반서비스 앱 이용에 영향을 미치는 요인을 도출하고 이에 대한 인지된 위험의 영향력을 확인하고자 하였다. 이를 위해 위치기반서비스 앱 이용 행위에 영향을 미치는 변수를 도출한 후 인지된 위험이 이들 변수와 앱 이용 행위 사이에 어떤 영향을 미치는가를 실증 분석하였다. 분석 결과, 유용성과 사회적 영향은 위치기반서비스 앱 이용에 영향을 미치나, 인지된 위험을 높게 인식하는 집단은 유용성으로부터, 인지된 위험을 낮게 인식하는 집단은 사회적 영향으로부터 이용 행위에 더 크게 영향을 받는 것으로 나타났다. 위치기반서비스 산업의 성장을 위해서는 인지된 위험의 교섭력을 최소화하기 위한 정보보호 정책과 보호기술이 요구되며 기업 입장에서는 인지된 위험 수준에 따른 개발 전략을 취해야 할 것이다. As the disclosure of privacy information has grown steadily, concerns about mobile services based on the personal information also increased. We aspired to reveal factors influencing the use of Location-Based services(LBS) App and analyse how the perceived risk affected between these factors and the use of LBS App. Results showed that usefulness and social influence influenced on the use of LBS App. We also found that the group who highly recognized the perceived risk was highly affected by usefulness and the group who lowly recognized the perceived risk was highly affected by social influence. Findings show that the company's strategy should be different depending on the level of consumers' perceived risk.

Privacy quantification model based on the Bayes conditional risk in Location-Based Services

The widespread use of Location-Based Services (LBSs), which allows untrusted service providers to collect large quantities of information regarding users' locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms (LPPMs) have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary's estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

Exploration of Location-Based Services Adoption

The purpose of this paper is to explore aspects of user perceptions of their use of location-based services. As mobile technologies become more ubiquitous in the general population, it is reasonable to assume that individuals will consume services and software to enhance their aspirations and entertainment desires. This study begins by constructing a location-based service prototype simulation. It then conducts an experiment and analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A survey was developed to extract usage information from participants, followed by an analysis of the results using PLS. The analysis shows significant indicators that suggest behaviour patterns of early adopters of location-based services are being observed. This paper applies the UTAUT model using a location-based service experiment to understand the underlying perceptions of individuals who may adopt location-based services. The authors study the effects of multiple parameters on the use of a location-based service simulation. Through this simulation and a following survey, current perceptions of LBS are investigated and insights gained.

Surveillance in ubiquitous network societies: normative conflicts related to the consumer in-store supermarket experience in the context of the Internet of Things

The Internet of Things (IoT) is an emerging global infrastructure that employs wireless sensors to collect, store, and exchange data. Increasingly, applications for marketing and advertising have been articulated as a means to enhance the consumer shopping experience, in addition to improving efficiency. However, privacy advocates have challenged the mass aggregation of personally-identifiable information in databases and geotracking, the use of location-based services to identify one's precise location over time. This paper employs the framework of contextual integrity related to privacy developed by Nissenbaum (Privacy in context: technology, policy, and the integrity of social life. Stanford University Press, Stanford, 2010) as a tool to understand citizen response to implementation IoT-related technology in the supermarket. The purpose of the study was to identify and understand specific changes in information practices brought about by the IoT that may be perceived as privacy violations. Citizens were interviewed, read a scenario of near-term IoT implementation, and were asked to reflect on changes in the key actors involved, information attributes, and principles of transmission. Areas where new practices may occur with the IoT were then highlighted as potential problems (privacy violations). Issues identified included the mining of medical data, invasive targeted advertising, and loss of autonomy through marketing profiles or personal affect monitoring. While there were numerous aspects deemed desirable by the participants, some developments appeared to tip the balance between consumer benefit and corporate gain. This surveillance power creates an imbalance between the consumer and the corporation that may also impact individual autonomy. The ethical dimensions of this problem are discussed.

Exploring News Apps and Location-Based Services on the Smartphone

This study investigates how young adults use news and location-based services on their smartphones, in addition to examining how many news organizations offer mobile news apps with geo-location features. Based on the survey findings, young adults are consuming news on their smartphones. Furthermore, there is a high use of location-based services by smartphone consumers, but news organizations are only using geo-location features in their mobile apps for traffic and weather. This study highlights that a gap exists between what news consumers, particularly young adults, are doing and using on their smartphones and what news organizations are able to provide.

Balancing trajectory privacy and data utility using a personalized anonymization model

With the widespread use of location-based services (LBS), the number of trajectories gathered by location service providers is dynamically growing. On the one hand, mining and analyzing these spatiotemporal trajectories can help to work out a mobile-related strategic planning; on the other hand, knowledge of each trajectory can be used by adversaries to identify the user's sensitive information and lead to an unpredictable harm. The concept of trajectory k-anonymity extends from location k-anonymity that has been widely used to address this issue. The main challenge of trajectory k-anonymity is the selection of a trajectory k-anonymity set. However, existing anonymity methods ignore the trajectory similarity and direction, assuming that it has little impact on privacy. Thus, it cannot provide a preferable trajectory k-anonymity set. In this paper, we propose to use trajectory angle to evaluate trajectory similarity and direction, and construct an anonymity region on the basis of trajectory distance. Considering the various preference settings on the proportion of trajectory privacy and data utility in different scenarios, we propose a personalized anonymization model to select the trajectory k-anonymity set. Experiment results prove that our method can provide an effective trajectory k-anonymity set under various proportions of trajectory privacy and data utility requirements, while the efficiency just reduces a little.