Location-based Services System Research Articles

Lightweight consortium blockchain-enabled secured Vehicular ad Hoc Network using certificateless conditional privacy-preserving authentication mechanism.

Towards the intelligent transportation systems, Location Based Service (LBS) are widely engaged in Vehicular Ad Hoc Networks (VANETs) that are becoming as significant application that change the human driving experience in today's world. LBS systems facilitate the users with intelligent services by collecting an accurate location information. Due to the frequent exchange rate of the location information in an open environment, VANETs are inherently susceptible to privacy and security attacks. In past, many schemes have been proposed to ensure the privacy and security of exchanged location information; but fail to deploy in practical VANETs. At the same time, system efficiency is compromised which is another primary requirement of VANETs. Leveraging the semi-decentralized and lightweight nature of consortium blockchain technology, and Certificateless conditional privacy protection scheme to reduce the node authentication overhead, this paper introduces Consortium Blockchain assisted Certificateless Conditional Privacy Protection scheme to address the aforementioned challenges. Additionally, the proposed scheme has ability to develop anonymous regions for a particular time stamp ensuring the location privacy of vehicles. Rigorous security analysis and experiments show the practicality and resilience to various attack models, and achieve ADP 83% with maximum malicious attacks. Comparing with existing state of the art methods, the proposed scheme exhibits the privacy improvement and low computational complexity.

EUAC: An Advanced Privacy Protection for Location-based Service System

EUAC: An Advanced Privacy Protection for Location-based Service System

A Location-Privacy-Aware Taxi-Hailing System: Adaptive Differential Privacy-Based Dynamic Incentive Method

Nowadays location-based service (LBS) has become an important service in people’s daily life. Online taxi-hailing system (DiDi, Uber, etc.) is one of the most common LBS system. As the scale of online taxi services continues to expand, some problems have gradually emerged. Specifically, the uncertainty of taxis and passengers makes it difficult to match them effectively, and the passengers’ location privacy will be threatened from both internal and external of the system. In this paper, we first proposed a Bayesian-based location privacy inference attack method from external adversary’s point of view. After that, an adaptive differential privacy-based dynamic incentive method was proposed. Firstly, an adaptivity clocking area division method was proposed to resist the internal privacy threat. Secondly, a dynamic incentive bidding method was proposed to deal with the trading issue. Thirdly, an exponential-based matching method was proposed to resist the external inference privacy threat. Further, theoretical proofs show that the proposed method satisfy the privacy properties of k-anonymity, 2μ1ε-differential privacy, and the economic properties of incentive compatibility, individual rationality. Finally, the experimental results show that the inference attack would achieve a maximum attack success rate of 95% while ensuring the accuracy within 150 meters, and the proposed adaptive differential privacy-based dynamic incentive method can not only provide a good economic performance in terms of satisfaction ratio, social welfare and travel distance, but also can resist internal privacy threat with less than 1% of privacy leakage probability and reduce the success rate of external privacy inference attacks to 25%.

FastReach: A system for privacy-preserving reachability queries over location data

Reachability, which indicates whether a person is reachable from another person through a sequence of location-based contacts, is of importance in many domains, such as public health. Nowadays, large amounts of location data are collected by various Location-Based Services (LBS), which helps to improve the accuracy of reachability queries. Meanwhile, as many LBS systems are deployed on cloud platforms, users have huge concerns about the leakage of their location data. In this paper, we consider how to implement privacy-preserving reachability queries over private location data. We first propose SecReach, a system which can process secure reachability queries by applying Bloom filters and Homomorphic Encryption. Based on the main idea of SecReach, we propose a more efficient system, FastReach, that significantly reduces the computation costs by using Single Instruction Multiple Data operations and Z-order index. We prove the security of our systems rigorously, and demonstrate their efficiency comprehensively.

Location Based Service for Important Locations in the Paser Belengkong District

Location-based service systems or better known as Location-Based Services (LBS) are information services in the form of geographic information accessed using a smartphone via a mobile network connection for precise mapping. This research focuses on profile information and routes for office locations, places of worship, health, schools, emergency places, markets, sports facilities and infrastructure, companies and ATMs in Paser Belengkong District with location points that utilize the Location Manager Application Programming Interface technology. (API maps) and Location Provider (API Location) from Google. The data collection method used was interviews by asking questions related to important location data in Paser Belengkong District. By way of observation, namely by making direct observations of the villages in Paser Belengkong District. In this research the system development method uses the waterfall and the testing techniques used are Black Box and Beta Testing. The supporting software used is Android Studio, Xampp, Sublime Text, Google Maps and software design using the Unified Modeling Language (UML) modeling system, Use Case Diagrams, Activity Diagrams, Sequence Diagrams and Class Diagrams. Test results by utilizing Location Based Service Technology which combines Geographic Information System, Internet Service

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.

Efficient deniable authentication and its application in location-based services

Deniable authentication (DA) assures message authentication between sets of participants. The actual participants can be certain about the messages’ authenticity but cannot prove it to any third party after the event. In applications such as location-based service (LBS) systems, DA is inevitably relevant to assure the coercion-free state of its usage and to protect user privacy. To protect user privacy in LBS systems, we present two heterogeneous deniable authentication (HDA) schemes called HDA-I and HDA-II scheme. The HDA-I scheme permits communication sessions from certificateless cryptography (CLC) to identity-based cryptography (IBC) setting, whiles the HDA-II scheme runs in a vice versa mode. Our schemes admit security proof in random oracle model (ROM) under computational Diffie–Hellman (CDH) and bilinear Diffie–Hellman (BDH) assumptions. Furthermore, both HDA-I and HDA-II provide batch verification, that can accelerate the verification of authenticators. Lastly, via comprehensive performance evaluation, our schemes show up efficient against other recent related schemes.

MQTT Protocol Extension for Real Time Location based Service

This paper introduces an extension of the MQTT protocol to reduce communication overhead in real-time location-based service system where mobile MQTT clients move and change their administrative district location frequently. The MQTT protocol was extended to handle UNSUBSCRIBE and SUBSCRIBE processing with a single step and the LBS server was revised to handle the extended protocol. Instead of directly processing SUBSCRIBE and UNSUBSCRIBE, mobile clients need to send their GPS location to the LBS server. LBS Server communicate with MQTT Broker to using extended protocol on behalf of each client. In an environment where large number of clients frequently move, the extended MQTT protocol can drastically reduce the number of messages between MQTT Broker and clients. This is mainly because clients do not need to issue SUBSCRIBE and UNSUBSCRIBE request to MQTT Broker. In addition, in contrast to our previous study where clients receive new administrative area name after sending its GPS location to LBS server, in this research, using extended MQTT protocol, mobile clients do not need to receive new administrative name even though they move across different district areas and as a result, the number of message exchanges can also be reduced. The LBS system applying the extended MQTT protocol can be used as a real-time location-based information service for large-scale mobile devices, such as real-time pedestrian population and vehicle traffic analysis, and location-based message delivery.

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.

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems.

Highly Efficient Privacy Preserving Location-Based Services with Enhanced One-Round Blind Filter

To take advantages of location-based services (LBS) while protecting user privacy against untrusted LBS providers, privacy preserving LBS have attracted increasing attention. Considering that users in an LBS system are often equipped with resource-constrained mobile devices, most existing privacy preserving LBS methods are based on anonymization techniques. However, these existing schemes still have some privacy and efficiency limitations. In this paper, we propose a novel privacy preserving LBS scheme, which simultaneously achieves user privacy protection and high query efficiency. Specifically, we utilize the k-anonymity technique and the pseudo random function to protect the location privacy and the query message privacy of users. We design an enhanced one-round blind filter protocol (ORBFe) based on the Paillier cryptosystem to securely filter out redundant records generated by the k-anonymity technique. Compared with existing solutions, our ORBFe protocol not only ensures that users receive exactly satisfying results but also incurs a low computation and communication cost on the server side. Through the theoretical analysis and extensive experiments, we demonstrate the security and efficiency of our proposed scheme.

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.

DPLQ: Location‐based service privacy protection scheme based on differential privacy

The existing privacy protection schemes for Location-Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial-temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k-means algorithm and l-diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k-anonymous algorithm, the users' query privacy protection algorithm builds a query k-anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k-anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.

Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data

The dynamic monitoring of home and workplace distribution is a fundamental building block for improving location-based service systems in fast-developing cities worldwide. Inferring these places is challenging; existing approaches rely on labor-intensive and untimely survey data or ad hoc heuristic assignment rules based on the frequency of appearance at given locations. Motivated by the regularities in human behavior, we propose a novel method to infer the home, workplace, and third place based on an individual’s spatial-temporal patterns inferred from Call Detail Records. To capture the individual regularity, our method develops, for each person-location, the probability distribution that the person will appear in that location at a specific time of day using geo-temporal travel patterns a panel of individuals. To reveal the collective regularity, we apply eigen-decomposition to the matrix of historical geo-temporal data. Unsupervised machine learning techniques are then used to extract commonalities across locations for different groups of travelers, making inferences, such as home and workplace. Testing the methodology on real-world data with known location labels shows that our method identifies home and workplace with significant accuracy, improving upon the best practices in the literature by 79% and 34%, respectively. The methodology proposed is computationally efficient and is highly scalable to other real-world applications with historical tracking data. It provides a basis to improve location-based services, such as mobile commerce, social events recommendations, and urban transit design.

Privacy Preserving Physical Layer Authentication Scheme for LBS based Wireless Networks

With the fast development in services related to localisation, location-based service (LBS) gains more importance amongst all the mobile wireless services. To avail the service in the LBS system, information about the location and identity of the user has to be provided to the service provider. The service provider authenticates the user based on their identity and location before providing services. In general, sharing location information and preserving the user’s privacy is a highly challenging task in conventional authentication techniques. To resolve these challenges in authenticating the users, retaining users’ privacy, a new SVD (singular value decomposition) based Privacy Preserved Location Authentication Scheme (SPPLAS) has been proposed. In this proposed method, physical layer signatures such as channel state information (CSI) and carrier frequency offset (CFO) are used for generating secret key required for encrypting the user’s location and identity information, and thus encrypted user’s information is sent to service provider for authentication. Secret key is generated by applying SVD on CSI vector. The proposed scheme aids in authenticating the user through location information while protecting the user’s privacy. The performance of the proposed method is evaluated in terms of bit mismatch, leakage and bit error rate performance of receiver and adversary. The simulation results show that the proposed scheme achieves better robustness and security than the existing location-based authentication techniques.

Community Public Safety Evaluation System Based on Location Information Service Architecture

The establishment of communities is for the better operation and management of cities, and community safety has gradually become a topic of increasing concern. It is not only an important part of the community public safety system, but also the foundation of social and economic development. With the improvement of the overall economic level of society, wireless network coverage has been greatly improved, smart devices have also been rapidly developed and popularized, and location-based information service systems have gradually entered the field of community public safety. At present, my country still lacks experience in community safety management. At the same time, there is no complete set of standards and evaluation system for the public safety evaluation system of urban communities. However, community public safety involves people’s livelihood issues and the stability and development of the city. Therefore, it is urgent to build a community public safety evaluation system based on the location information service architecture. Based on the safety community research and location service system, this paper uses the combination of theory and practice, quantitative and qualitative methods, and tentatively explored and constructed a community public safety evaluation system based on the location information service architecture. This article integrates location information service technology into the community public safety evaluation system, selects three indicator factors, investigates a more comprehensive evaluation subject, and combines the evaluation subject’s qualitative evaluation of community public safety with quantitative evaluation through fuzzy comprehensive evaluation. This paper conducts an empirical analysis of a certain community and finds that its public safety evaluation score is 62.6232 points, which is in the “general” ranks, and it needs to be combined with the location information service system to improve the community safety issues. This study has guiding significance for ensuring community public safety and has theoretical and practical significance for community construction and development.

Heterogeneous deniable authenticated encryption for location-based services.

The location-based services can provide users with the requested location information. But users also need to disclose their current location to the location-based service provider. Therefore, how to protect user’s location privacy is a major concern. In this paper, we propose a heterogeneous deniable authenticated encryption scheme called HDAE for location-based services. The proposed scheme permits a sender in a public key infrastructure environment to transmit a message to a receiver in an identity-based environment. Our design utilizes a hybrid encryption method combing the tag-key encapsulation mechanism (tag-KEM) and the data encapsulation mechanism (DEM), which is well adopted for location-based services applications. We give how to design an HDAE scheme utilizing a heterogeneous deniable authenticated tag-KEM (HDATK) and a DEM. We also construct an HDATK scheme and provide security proof in the random oracle model. Comprehensive analysis shows that our scheme is efficient and secure. In addition, we give an application of the HDAE to a location-based services system.

Location Based Services Acceptance and Adoption Evaluation: An Investigation on Mobile Phone Users’ Response to Awareness and Utilization of LBS Technology in Nairobi Kenya

Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplementary information on goods and services are tagged. The navigation services have become popular and can be installed on mobile phones to provide route information, location of points of interest and user’s current location. LBS has continued to face challenges which include “communication” process towards user reference. Location Based Service System conveys suitable information through a mobile device for effective decision making and reaction within a given time span. This research was geared at understanding the state of LBS technology acceptance and adoption by users in Nairobi Kenya. To do this a quantitative study was carried out through a questionnaire, to investigate mobile phone users’ response on awareness and use of LBS technology. Testing the growth of this technology in this region compared to predictions in previous studies using Technology Acceptance Model (TAM), it is evident that many users may be aware of GPS functionality in mobile phones but are certainly yet to fully embrace the technology as they rarely use it. This points to some underlying challenges towards this technology within this part of the World, thereby recommending for deliberate monitoring and evaluation of LBS technology for sustenance growth based on user satisfaction and acceptance for improved usability.

Secure and location privacy in geographical data with electronic codebook mode-advanced encryption standard

Location-Based Service (LBS) is considered as one of the utmost encouraging as well as new ways of convergence technologies ensuing in the development of numerous fields together with database systems, mobile communication, internet technology and positioning systems. Even though being introduced in the mid 1990s, only contemporary LBS has achieved an efficient intense study owing to its commercial and scientific influence. As the LBS is associated to the user's location that can be preferred to track the actions of user, a strong privacy concern is elevated. To maintain the user's position, different intelligent works that are introduced with numerous challenges are still expecting solutions. To solve these issues, this paper proposes an encryption technique for improving the security of the LBS system named as Electronic Codebook Mode-Advanced Encryption Standard (ECBM-AES). The key purpose of this study is to offer security in addition to location privacy against the attackers. The cryptographic approach uses two veriers, and the claimed region could be in any intermission amongst the two veriers. The results show that the proposed technique provides better key generation ratio and network size, Packet Delivery Ratio (PDR), security and higher location privacy compared with the existing techniques.

Predicting user’s next location using machine learning algorithms

The reliability on smartphones has been increased in a tremendous way in the last decade, and this incensement brings us nothing but data to investigate. Furthermore, the ability to predict user’s next location plays a vital role in Location-Based Services and recommender systems. This paper is seeking to predict the user’s next location based on their spatial background using machine learning methods like Artificial Neural Networks and Classification methods like K-Nearest Neighbors (KNN), Support Vector Machine and Decision Tree. The suitable method is then chosen through their comparison. The data used in this research is from active users provided from the Geolife dataset from the city of Beijing. The best result is obtained from the Weighted K-Nearest Neighbors (KNN) method, with an overall accuracy of 91.98 percent. Additionally the dependency between observed data and model prediction is determined. A concept called Routineness is also introduced, which shows the predictability and anomalies of each users’ behavior, based on the difference in prediction methods from their spatial and temporal background. A comparison is also done with similar researches that used the same data to evaluate the sufficiency of the methods. The computation shows the proposed method predicts behavior 2.72% more accurate than similar ones.