Location-based Services Users Research Articles

Location Privacy Protection Game Against Adversary Through Multi-User Cooperative Obfuscation

In location-based services(LBSs), it is promising for users to crowdsource and share their Point-of-Interest(PoI) information with each other in a common cache to reduce query frequency and preserve location privacy. Yet most studies on multi-user privacy preservation overlook the opportunity of leveraging their service flexibility. This paper is the first to study multiple users' strategic cooperation against an adversary's optimal inference attack, by leveraging mutual service flexibility. We formulate the multi-user privacy cooperation against the adversary as a max-min adversarial game and solve it in a linear program. Unlike the vast literature, even if a user finds the cached information useful, we prove it beneficial to still query the platform to further confuse the adversary. As the linear program's computational complexity still increases superlinearly with the number of users' possible locations, we propose a binary obfuscation scheme in two opposite spatial directions to achieve guaranteed performance with only constant complexity. Perhaps surprisingly, a user with a greater service flexibility should query with a less obfuscated location to add confusion. Finally, we provide guidance on the optimal query sequence among LBS users. Simulation results show that our crowdsourced privacy protection scheme greatly improves users' privacy as compared with existing approaches.

Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

The development of new mobile communication and information service technologies has opened up exciting possibilities for location-based services. Users of location-based services (LBS) can access vital data from their service providers by utilizing their location data. Maps and navigation, information services, tourist information services, social networking, and many more popular applications are available. A user's location and other personal details must be submitted to the providers of location-based services in order for them to work. For example, details about one's whereabouts and identity. By "location privacy," we mean the idea that third parties shouldn't be able to track a user's precise whereabouts. It is important that users' sensitive information be hidden from unauthorized individuals when communicating. Most difficult in LBS location-based are concerns about communications and data. Each peer does their duty reciprocally in a collaborative method, which is a completely distributed technique. For the most secure and private location-based services (LBS), it employs cryptographic methods. The number of people using LBS is growing at a rapid pace these days. At this time, there isn't a single method available that has scalability capabilities. Building a realistic and computationally efficient solution that offers high privacy while decreasing processing overhead and improving scalability is a challenging task. The suggested method is cost-effective, supports scaling, is highly resilient against security and privacy assaults, and ensures privacy.

Location-Based Services for Indoor Environments: A User Experience Test

The location-based services (LBS) user experience in controlled indoor settings in this scientific research. The environments included a range of user demographics, device configurations, and ambient factors. According to our findings, customer satisfaction ratings ranged from 5 to 9 on a scale of 1 to 10, and the average location accuracy was 1.1 meters (±0.4 meters) across various interactions and locales. These conclusions were supported by qualitative comments, which expressed satisfaction with the effectiveness of the check-in procedures and the practicality of several LBS features. Negative reviews, on the other hand, revealed difficulties with positioning technology accuracy, highlighting the intricate interactions among variables influencing indoor location-based services user experiences. This study provides a solid scientific basis for indoor location-based services (LBS) optimization, highlighting the need of tackling practical issues to improve user contentment and the usefulness of such services in indoor environments.

Application of Geolocation Module in the Electronic Multimedia Guide

The paper presents the work in progress towards the enhancement of features and increasing the comfort of usability of the multimedia guide for the War Museum of Niš under the Open Sky. Previously, this electronic guide was realized as a mobile application that informs visitors about the persons and events to which monuments in the city of Niš are dedicated. Visitors could access information about the monuments through a list of monuments or by using an interactive map with the location of the monuments marked. Presently, we are introducing a new module that will improve user experience through the usage of location-based services. The visitor's location is used by the Geolocation module to show the information about the nearby monument. The monument has to be inside of a predefined radius (e.g. 2 m or more) in order to activate the projection of the information about the monument.

Using Dummy Locations to Conceal Whereabouts of Mobile Users in Location-Based Services

Location-based services are extremely popular in these days. Mobile users use the services such as GPS and Google Maps almost every day to assist their daily activities like finding a restaurant or looking for an apartment. However, in order to use the services, users have to share their location information with the service providers. This requirement may hold back the service adaptation since users may not like to share their locations with others. One common method to preserve user privacy is to send a couple of dummy locations along with the true location to the service providers, so the provider would not be able to tell which location is true. This method is simple and effective, but it also has some drawbacks that make the privacy safeguarding fragile. If the dummy locations are not carefully planned, they may land on wild fields or water and could be easily perceived as fake. This research investigates the flaws of using dummy locations to uphold user privacy from both the users’ and service providers’ points of view, and proposes innovative methods to close the loopholes, so more users will be willing to use location-based services.

Utility-Aware and Privacy-Preserving Mobile Query Services

Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users' locations to untrusted LBS providers may lead to privacy concerns. This paper proposes StarCloak, a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. StarCloak combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, StarCloak uses the concept of stars and proposes cloaking graphs for effective location cloaking on road networks. Second, StarCloak supports user-specified k-user anonymity and <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$l$</tex></formula> -segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, StarCloak achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two datasets using real-world road networks, under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar, its most relevant competitor.

Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

Cloud Edge-Client Collaborative Trajectory Privacy Protection System and Technology

Location-based service (LBS), with its personalized, real time, and mobile features, is one of the more popular mobile applications (apps) in the world today. However, under the centralized LBS architecture, the high dimensional trajectory data collected from LBS users are directly exposed to the central server, which entails serious privacy risks. Although existing privacy protection technologies can protect the private user trajectories to a certain extent, they often ignore the reasonable data requirements of LBS providers (e.g., data required to maintain or improve services). Focusing on the weaknesses of centralized LBS and the shortcomings of existing solutions, this article proposes a cloud edge-client collaborative trajectory privacy protection system. The system migrates LBS from the cloud to the network edge and balances privacy and utility-including service utility and data utility-with anonymous authentication, dummy location, and privacy risk evaluation mechanisms. The theoretical analysis and simulation results show that the system can effectively protect trajectory privacy under the premise of high availability of services and data, which is a significant improvement over the current LBS system.

User Sensitive Information Protection Scheme Based on Blockchain Technology

With the continuous development of mobile applications and location technology, k -anonymity technology is usually used to protect the privacy and security of users in location-based services that are used by mobile users. Due to the self-interest of users, in the cooperative construction of a k -anonymity privacy protection scheme, it is difficult to guarantee the honest cooperation behavior of the requesting and cooperating users. To solve the above problems, this paper proposes a distributed k -anonymous location privacy protection scheme based on blockchain. In this paper, we record the requesting users, cooperative users, and their location information as evidence, and we punish the users with location leakage and deception. An anonymous zone cannot be constructed when the user is a requesting user. An interactive record mechanism is designed to constrain the self-interest behavior of the requesting and cooperating users in the process of anonymous region construction. Security analysis and experiments show that this scheme can effectively protect the user’s location privacy, promote the honest cooperation between the requesting user and the cooperative user, and encourage more users to participate in the construction of an anonymous zone.

Gains-focused vs risk-averse orientations and their impact on location-based marketing services in tourism

对比以收益为中心与风险规避为中心的不同取向, 及其在旅游中对定位营销服务的影响研究目的调节焦点理论提出, 消费者决策以及目标达成行为可以基于对收益/非收益敏感的促进目标或反映对损失/非损失敏感度的保守目标（Aaker and Lee, 2001）。本研究的目的是研究调节焦点及其对定位营销在旅行体验中的感知价值和使用的影响。研究设计/方法/方法旅行计划策略是按照促进或保守路线制定来评估调节的取向, 并检查受访者对定位营销(LBM) 服务价值的看法。本研究运用回归分析评估主要关系, 然后分析评估“旅行兴趣信息”（折扣券等）在LBM关系中的调节焦点感知价值中的中介作用。研究发现调查结果表明, 促进型路线的效果明显大于保守型的效果。此外, 旅游兴趣信息的重要性（折扣券、地图等）在促进调节焦点（相比于保守型）中显得尤其重要。实践意义研究结果证明了调节焦点是旅游消费行为的差异化因素, 并展示了如何将其用于有效地定位营销信息以增强旅游体验。目的地营销组织可以在领导力、决策制定和一线员工创造力等领域的组织环境中应用调节焦点理论, 从而提供难忘的旅行体验。研究原创性/价值本论文是首次研究以收益为中心/风险规避为中心的旅行计划, 以及其在旅行经历中对定位营销服务计划中价值感知的影响之间的关系的研究之一。

Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society. In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic. LBS users use their real location to build LBS queries to take benefits. This makes location privacy vulnerable to attacks. The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible. Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks. In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks. The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations. Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other. Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks. The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process. In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec.

A process calculus for privacy-preserving protocols in location-based service systems

Location privacy-preserving mechanisms (LPPM) were introduced to protect users of location-based services (LBS) from the privacy threat of leaked personal data. These mechanisms alter the communication to the LBS in such a way that no malicious party can derive sensitive personal data. In the literature one can find many different LPPMs with varying privacy guarantees but so far no formal framework exists for comparing all of these mechanisms and their privacy guarantees. We propose the σ-calculus, which enables the modeling of LPPMs and location privacy guarantees. The calculus entails a process language, a property language, and a temporal-K-less-epistemic modal logic that enables the verification of location privacy properties on process models. The σ-calculus is a novel process calculus that is based on standard set operations that are computationally cheap, yet sufficient for modeling privacy violations in LBS communication. The calculus is the first formal framework to model and reason about a wide array of LPPMs. Furthermore, we present model checking algorithms that enable the automatic verification of location privacy properties. We evaluate the expressiveness of our process language by modeling a significant number of LPPMs from the literature and the expressiveness of our property language by describing a benchmark set of location privacy properties. Furthermore, we demonstrate the verification of a privacy property with an extended example of an LPPM algorithm designer. We developed the tool LP3Verif that implements our model checking algorithms. The open-source tool is written in Java and uses the SMT solver CVC4 as back end.

Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.

JLGBMLoc-A Novel High-Precision Indoor Localization Method Based on LightGBM.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on received signal strength (RSS), is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm—named joint denoising auto-encoder (JDAE)—which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on the UJIIndoorLoc dataset and the Tampere dataset, the experimental results show that the proposed model increases the positioning accuracy dramatically compared with other existing methods.

In-store shopping with location-based retail apps: perceived value, consumer response, and the moderating effect of flow

Bricks-and-mortar retailers have recently begun to utilize mobile applications delivering location-based services (LBS) as part of their omni-channel strategy to provide consumers with new in-store experiences. In light of this trend, this study examined how consumers’ value perception influences their intention to use LBS in the store and their behavioral responses as well as the moderating effect of flow on the relationships between the perceived benefits/costs and the perceived value of LBS usage. The results indicated that benefits (perceived usefulness and perceived enjoyment) and costs (perceived complexity and perceived privacy risk) were influential antecedents shaping consumers’ value perception of LBS, which in turn impacted their intention to use LBS and behavioral responses (search and purchasing using LBS). Also, we found that the negative relationship between the perceived costs and perceived value was attenuated in high flow states than in low flow states

Understanding vehicular routing behavior with location-based service data

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.

Deloc: a delegation-based privacy-preserving mechanism for location-based services

Location-based services (LBSs) are everywhere; we found them in several fields such as healthcare, entertainment, transportation, and many other daily activities. Besides, along with their presence in almost every daily task, their usefulness cannot be ignored. Moreover, with smartphone ownership growth, getting one's location became easier, and the privacy-related issues became almost inescapable. Hence, more severe solutions are strongly required to handle privacy issues and to keep LBS utility. We present in this paper Deloc, a novel location privacy protection mechanism founded on the idea of preserving location privacy without altering geographical coordinates. We introduce a new set of concepts and definitions that allow us to meet high privacy guarantees and data accuracy by delegating one's request to other LBS users. Similarly, we investigate the privacy issues related to LBSs along with state-of-art protection approaches and techniques. We evaluate Deloc on both synthetic and real-world data in a finely simulated environment with tunable parameters, and demonstrate its efficiency and usefulness in today's LBS applications, along with its higher privacy guarantees in respect to differential privacy standards.

Location Privacy Protection via Delocalization in 5G Mobile Edge Computing Environment

In this paper, we propose LBS@E, a new architecture for location-based services (LBSs) facilitated by the mobile edge computing paradigm. LBS@E tackles the location privacy problem innovatively by delocalizing LBSs so that mobile users of LBSs implemented based on LBS@E do not have to reveal their locations. They retrieve local information from nearby edge servers around them instead of the cloud. In this way, we resolve the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile users location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. We model this problem as a constrained optimization problem and propose an optimal approach for solving it based on integer programming. Extensive experiments are conducted on a widely-used real-world dataset to demonstrate effectiveness and efficiency.

Entity Alignment Method of Points of Interest for Internet Location-Based Services

In Internet applications, the description for the same point of interest (POI) entity for different location-based services (LBSs) is not completely identical. The POI entity information in a single LBS data source contains incomplete data and exhibits insufficient objectivity. Aligning and consolidating POI entities from various LBSs can provide users with more comprehensive, objective, and authoritative POI information. We herein propose a multi-attribute measurement-based entity alignment method for Internet LBSs to achieve POI entity alignment and data consolidation. This method is based on multi-attribute information (geographical information, text coincidence information, semantic information) of POI entities and is combined with different measurement methods to calculate the similarity of candidate entity pairs. Considering the demand for computational efficiency, the particle swarm optimization algorithm is used to train the model and optimize the weights of multi-attribute measurements. A consolidation strategy is designed for the LBS text data and user rating data from different sources to obtain more comprehensive and objective information. The experimental results show that, compared with other baseline models, the POI alignment method based on multi-attribute measurement performed the best. Using this method, the information of POI entities in multisource LBS can be integrated to serve netizens.

Deloc: Delegation-Based Privacy-Preserving Mechanism for Location-Based Services

Location-based services (LBSs) are everywhere; we found them in several fields such as healthcare, entertainment, transportation, and many other daily activities. Besides, along with their presence in almost every daily task, their usefulness cannot be ignored. Moreover, with smartphone ownership growth, getting one's location became easier, and the privacy-related issues became almost inescapable. Hence, more severe solutions are strongly required to handle privacy issues and to keep LBS utility. We present in this paper Deloc, a novel location privacy protection mechanism founded on the idea of preserving location privacy without altering geographical coordinates. We introduce a new set of concepts and definitions that allow us to meet high privacy guarantees and data accuracy by delegating one's request to other LBS users. Similarly, we investigate the privacy issues related to LBSs along with state-of-art protection approaches and techniques. We evaluate Deloc on both synthetic and real-world data in a finely simulated environment with tunable parameters, and demonstrate its efficiency and usefulness in today's LBS applications, along with its higher privacy guarantees in respect to differential privacy standards.