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In this paper, we propose a control process for a service robot that runs errands for personal belongings using technology elements for Human-Robot Interaction (HRI). Initially, it classifies each space on a grid map mapped based on the indoor environment using Generalized Voronoi Graph (GVG) and registers the location of objects in each classified space, forming a topological map. Subsequently, it utilizes Google’s Speech To Text (STT) to identify voice commands, and integrates this information with the object details on the topological map to automatically generate feasible destinations for the robot based on the estimated location of the object. In an experiment involving instructing the robot with three voice commands for five types of personal belongings located within a simulated household environment, the system achieved a success rate of over 90% in accomplishing errands to the specified locations. This confirms the feasibility of the proposed system for location-based robot services. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user's errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services. [ABSTRACT FROM AUTHOR]

Most user-collaborative location privacy protection mechanisms assume that collaborative group members are trustworthy and can strictly enforce collaboration rules. Such assumptions do not match reality and reduce the usability of the schemes. In this paper, we propose Invisible Man, a blockchain-enabled framework for peer-to-peer collaborative privacy games for location-based services. The framework enables users to protect their privacy in extreme environments and allows members of a collaborative group without trust to collaborate efficiently. To defend against inference attacks, a user-collaborative privacy game model is constructed, and a dual-verification mechanism with Chainlink and Witnet oracles is developed to provide security guarantees for model generation. Members can conduct cooperative games under the guidance of the model. To realize secure and efficient in-group collaboration, a blockchain-based reward and punishment mechanism for collaboration is designed, integrating token incentives and a blacklisting mechanism to ensure the verifiability and audibility of user behaviors. Security analysis and extensive simulation results demonstrate that the proposed scheme achieves high security and privacy with low costs. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

Location based service (LBS) is related basically to a local, continuous, and spatially confined computation in the context-aware mobile environment. Hitherto, serving a query within the specified timeline becomes possible with the help of caching the data at the client and/or server sites. It enhances the performance of LBSs by the reduction in the network traffic, access latency, server load etc.; however, at the same time, it faces difficulty in maintaining the database consistency as it works in the environment, where frequent disconnections occur. As a result, many fascinating LBS caching issues created in the mobile environment have forced the mobile database specialists and researchers to do extensive research efforts to improve the response time and efficient storage resources by developing the effective location-based technologies. Thus, this study specifically discusses the aforementioned issues with comparative analysis over the common attributes such as highlighting limitations/strength, recent advancement etc. and also possible research directions for the further investigation of the unanswered questions. [ABSTRACT FROM AUTHOR]

Context-aware computing processes the mobile user's query/transaction submitted from anywhere at any time. Basically, location based services (LBSs) are continuous, local, and spatially confined applications of computing in the context-aware mobile environment, where queries/transactions are initiated by the mobile users. The smartphones as a resultant of today's advanced mobile technologies allow these mobile users to access numerous LBSs and provide information interactively to them depending on their locations. The mobile user's positions and associated confidential information enable more sensitive information to be created; but, it inevitably leads to a threat that these sensitive information may be used for different purposes by the third parties. Also, there is lack of state of the art location privacy preservation procedures to be able to create a balance between user location/ activity privacy preservation and quality of services in LBSs technologies. Therefore, there is a need to do more research efforts to ensure the privacy of these mobile users by developing the secured location-based technologies. Thus, our this study specifically discusses the aforementioned issues, a literature for the taxonomy of the privacy preservation approaches available to the research community with comparative analysis over the common attributes, highlighting limitations/strength, recent advancement and provides possible research directions for the further investigation of the unanswered questions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user’s errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services.

The use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’ location privacy, but it results in a large waste of communication overhead.The SpaceTwist scheme is an alternative method that uses an anchor point instead of the real location to complete the k-nearest neighbor query,which is simple to implement and has less waste of communication overhead.However,it cannot guarantee K-anonymous security, and the specific selection method of the anchor point is not provided.To address these shortcomings in SpaceTwist, some schemes calculate the user’s K-anonymity group by introducing a trusted anonymous server or using the way of user collaboration, and then enhance the end condition of the query algorithm to achieve K-anonymity security.Other schemes propose the anchor point optimization method based on the approximate distribution of interest points, which can further reduce the average communication overhead.A lightweight K-anonymity incremental nearest neighbor (LKINN) location privacy protection algorithm was proposed to improve SpaceTwist.LKINN used convex hull mathematical tool to calculate the key points of K-anonymity group, and proposed an anchor selection method based on it, achieving K-anonymity security with low computational and communication costs.LKINN was based on a hybrid location privacy protection architecture, making only semi-trusted security assumptions for all members of the system, which had lax security assumptions compared to some existing research schemes.Simulation results show that LKINN can prevent semi-trusted users from stealing the location privacy of normal users and has smaller query response time and communication overhead compare to some existing schemes.

The design of an efficient routing algorithm for the Internet of Vehicles (IoV) poses several problems due to its special characteristics, including nodes high-speed mobility, frequent topology change, link instability, and the presence of radio obstacles. Geographic routing protocols are a promising solution for the IoV, since the main routing parameter used by these protocols is the location information accessible through a location-based service. However, location services and traffic status measurements generate high network overhead. In this paper, we first propose a novel proactive location service called ATLAS that utilizes smart mobile agents to avoid the traffic overhead of traditional services and reduce the location update latency. Then, we present an Agent-Based Proactive Geographic Routing Protocol called ARDENT to route data packets with reduced delay and higher delivery ratio. The proposed systems aim to benefit from the IoV environment and multiagent characteristics to adapt to high-speed mobility, reduce the communication interruptions and the number of control messages overhead generated by the location service, and produce a complete geographic routing protocol suitable for urban settings. The evaluation of ATLAS shows significant performance improvements in terms of query success ratio and location update delay. In addition, the results of ARDENT illustrate its superiority in terms of data packet delivery ratio and end-to-end delay as compared to similar routing protocols. [ABSTRACT FROM AUTHOR]

To realize the great flexibility and cost savings for providing location-based service, data owners are incentivized to migrate their data to cloud servers. However, direct data outsourcing to untrusted servers may pose significant privacy risks. This paper proposes a practical privacy-preserving nearest neighbor searching method over encrypted spatial data. We simultaneously protect data and location privacy (access and pattern privacy) by encrypting data using asymmetric scalar-product-preserving encryption (ASPE) and performing computational private information retrieval (CPIR) on encrypted subspace datasets. To mitigate the performance degradation introduced by the combination of ASPE and CPIR, we propose a hierarchical index that enables users to safely obtain encrypted subspace datasets with configurable privacy, where different degrees of privacy can be traded off against query processing performance. Experiments demonstrate that our method outperforms the state-of-the-art method in efficiency while allowing for a flexible trade-off between performance and privacy. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.

The current global scenario is characterized by epidemics and various types of disasters, severely impacting communities' health, living conditions, and economic stability. Especially during such crises, the requirement for essential necessities becomes critical. Existing solution guidelines involve receiving donated items from public agencies through an offline system to provide assistance to victims. However, this implementation faces several limitations, such as a lack of understanding of people's needs in specific areas, leading to mismatches between assistance and actual requirements. Additionally, donators lack adequate information, resulting in further discrepancies between donated items and the victims' genuine needs. The available geospatial platforms primarily support surveillance and monitoring of epidemic or disaster situations but fail to address the management of needs related to donation and receipt. Through an extensive review of the literature and related research, it was identified that a geospatial platform specifically dedicated to donation management has not yet been developed. To address this gap, this research proposes the creation of a novel geospatial platform using data analysis and visualization methods, complemented by the integration of geofencing technology. By employing such an approach, the geospatial platform will play a pivotal role in enhancing disaster and epidemic assistance. It will facilitate efficient data processing and visualization, overcoming the limitations faced by the current offline system. Emphasis will be placed on improving the management of needs concerning donation and receipt, thereby mitigating mismatches between assistance and actual requirements. Consequently, this platform is expected to significantly enhance the effectiveness and targeting of aid distribution during times of crises. [ABSTRACT FROM AUTHOR]

The use of location-based service brings convenience to people's daily lives, but it also raises concerns about users' location privacy. In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users' location privacy, but it results in a large waste of communication overhead. The SpaceTwist scheme is an alternative method that uses an anchor point instead of the real location to complete the fc-nearest neighbor query, which is simple to implement and has less waste of communication overhead. However, it cannot guarantee K-anonymous security, and the specific selection method of the anchor point is not provided. To address these shortcomings in SpaceTwist, some schemes calculate the user's K-anonymity group by introducing a trusted anonymous server or using the way of user collaboration, and then enhance the end condition of the query algorithm to achieve K-anonymity security. Other schemes propose the anchor point optimization method based on the approximate distribution of interest points, which can further reduce the average communication overhead. A lightweight K-anonymity incremental nearest neighbor (LKINN) location privacy protection algorithm was proposed to improve SpaceTwist. LKINN used convex hull mathematical tool to calculate the key points of K-anonymity group, and proposed an anchor selection method based on it, achieving K-anonymity security with low computational and communication costs. LKINN was based on a hybrid location privacy protection architecture, making only semi-trusted security assumptions for all members of the system, which had lax security assumptions compared to some existing research schemes. Simulation results show that LKINN can prevent semi-trusted users from stealing the location privacy of normal users and has smaller query response time and communication overhead compare to some existing schemes. [ABSTRACT FROM AUTHOR]

The design of an efficient routing algorithm for Internet of Vehicles (IoV) is a challenging research issue due to the inherent characteristics of IoV network, such as high-speed mobility of nodes, frequent topology change, link instability, and the presence of radio obstacles. Geographic routing protocols are a promising solution for the IoV, since they are based mainly on the location information which can be easily obtained through a location-based service. However, location services and traffic status measurements generate high network overhead that overloads the network traffic and introduces routing latency and packet loss. In this paper, we first propose a novel lightweight location service that permits to discover all the geographical paths between two vehicles based on smart mobile agents. Second, we proposed ARGENT an Agent-Based Reactive Geographic Routing Protocol that couples the routing process with the novel lightweight location service. We leverage the IoV environment and multiagent characteristics to adapt to the high-speed mobility of vehicles, improve packet delivery, and reduce the number of control messages overhead. The performance evaluation shows significant improvements in terms of query success ratio, data packet delivery, and end-to-end delay while reducing communication interruptions and overhead.

Recently,with the development of Internet of things technology and the proposal of smart city concept,location-based service is developing rapidly,especially,the outdoor location services provided by global positioning system (GPS) based on satellite signals have penetrated into every aspect of out daily life.However,GPS is not applicable for indoor space due to the low localization accuracy,affected by the complex indoor environment.In order to improve the localization accuracy,indoor localization techniques are proposed.The techniques which utilize the existing devices such as Wi-Fi,low energy Bluetooth (BLE) are attracting more and more attention due to their advantages of low cost and easy deployment.This paper surveys the recent research work of low-cost indoor localization techniques with the basic motivations,implementations and their localization performance.Finally,the future development trend is prospected.

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs. [ABSTRACT FROM AUTHOR]

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept. [ABSTRACT FROM AUTHOR]

This paper proposes a new navigation modulation based on orthogonal frequency division multiplexing (OFDM). We derived the autocorrelation function and power spectral density of the OFDM modulation. The influence of the cyclic prefix and zero-padding is discussed. The influence of OFDM modulation parameters on navigation signal performance was deeply analyzed, which can help signal designers choose the OFDM parameters. The main peak of the proposed autocorrelation function is narrow and has good tracking accuracy. The sidelobe is lower, and the delay locking loop is more robust. The power spectrum density is evenly distributed in the main lobe of the signal, and the anti-interference is good. By comparing OFDM navigation signals with other navigation signals, it can be found that OFDM navigation signals have good tracking accuracy and a strong anti-interference ability. Combined with the proposed navigation modulation and communication signal, the OFDM navigation signal has a low bit error rate for the communication signal and has a good communication integration potential, which can meet the business requirements of the future communication and navigation integration market. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of location query and the effect of privacy protection in cloud environment, a location privacy protection method based on cipher‐text retrieval was proposed. Firstly, the spatial area is divided into several square grids, and the big Morton code of each location is calculated, the spatial area in the grid is divided again, and the small Morton code of each location is calculated. Secondly, the location data is encrypted by the combination of public key encryption and scrambling encryption, and then outsourced to the cloud server. When querying the location data of the interest points, the query request is sent to the data owner. After obtaining the query trapdoor and the private key, the query trapdoor is sent to the location data server for query. On the server side, the interest points in the region are found according to the cipher‐text matching, and the cipher‐text top‐k sorting is implemented. Finally, the cipher‐text query results are decrypted into plaintext by the private key. Experimental results show that the proposed method ensures the location data privacy, and improves query efficiency. At the same time, the balance between privacy protection security and query quality is achieved. [ABSTRACT FROM AUTHOR]

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m. [ABSTRACT FROM AUTHOR]

As the sensitivity of position, the privacy protection in both 5G positioning and its further application in location-based services (LBSs) has been paid special attention and studied. Solutions based on k-anonymity, homomorphic encryption, and secure multi-party computation have been proposed. However, these solutions either require a trusted third party or incur heavy overheads. Besides, there still lacks an integrated solution that can protect privacy for both positioning and LBS provision. Based on Intel SGX, this article proposes a novel light-weight scheme that can protect privacy in both 5G positioning and its further applications in LBS provision in an integrated way. Through secret sharing, the proposed scheme can also support multiple location-based service providers without frequent key exchange. We seriously analyze the security of our scheme. Based on scheme implementation, its efficiency is proved through the performance evaluation conducted over a real-world database. [ABSTRACT FROM AUTHOR]

Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular users’ location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality. [ABSTRACT FROM AUTHOR]

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions. [ABSTRACT FROM AUTHOR]

Summary: The frequent data leak scandals of recent years indicate that service providers who hold personal data may not be reliable as they claim. We assert that sensitive user information must be sanitized locally before it is sent to service providers if it is to be protected. The LPPK privacy‐preserving framework presented in this article is a local sanitization scheme, for location‐based services (LBSs). It applies a fog‐computing structure in which a private map is generated by the LBS server with kernel transformation for each user. A fog device then provides location services for each user according to the private map. Without colluding, neither the LBS server nor the fog device can deduce a user's real location. Experiments conducted on real‐world data sets demonstrate that LPPK delivers sufficient query accuracy at a level significantly higher than existing approaches while preserving location privacy. [ABSTRACT FROM AUTHOR]

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge. [ABSTRACT FROM AUTHOR]

Generally, a location‐based service (LBS) often contains the location attribute, content attribute, time‐stamp, and range. From the perspective of privacy protection, the location attribute and the content attribute are the key attributes and need to be protected. However, existing privacy protection methods focus excessively on the location attribute and ignore the content attribute contained in the LBS, which discloses the user's private information. In view of this challenge, a content‐aware privacy protection method, called the CPP method that considers the content attribute is proposed. Specifically, the CPP method is based on using k‐anonymity to generate dummy content attributes to protect the private content. As is shown in an experiment constructed on real‐world data, the CPP method can indeed improve the effect of privacy protection. [ABSTRACT FROM AUTHOR]

Aiming at the problem of users’ location privacy leakage caused by continuously using LBS, a road privacy level (RPL) algorithm was proposed based on road topological network, which divided the privacy level of the road sections around the sensitive locations.Then, a differential privacy location protection mechanism (DPLPM) was proposed.Privacy budget was allocated for sensitive road sections and Laplace noise was added to realize the privacy protection of location data.The experimental results show that the mechanism has high data availability while protecting the privacy of location information.

To solve the problem of real location leakage when vehicles use location-based service (LBS) on the Internet of vehicles, a location privacy protection scheme based on bus cache was proposed.Firstly, a point of interest (POI) pool was obtained from the LBS provider based on its route information.Then the data in the POI pool was selected form a POI list while driving.Finally, the POI list was broadcast to surrounding private vehicles.After the private vehicle received the broadcast data, it verified the identity of the bus and then stored the POI list in the vehicle’s local cache.When a private vehicle needed to query POI information, it would first retrieve it in the local cache, and if the cache was missed, it would send a query request to the LBS provider using the k-anonymity method.The simulation experiment results show that the proposed scheme can reduce the possibility of leakage of the real location of the private vehicle by reducing the number of communications between the private vehicle and the LBSP, thereby effectively improving the privacy protection level of the private vehicle.

Abstract—The growth of mobile features, especiallySmartphones at this time can be said to be very developed.Judging from the increasing number of mobile users, theavailability of various mobile features is also growing veryrapidly. In line with these facts, the author aims to create amobile boarding application using Location-Based Service(LBS) technology with the application of the Haversinemethod, making it easier for users to find the closest Dormitory to their place of work with appropriate facilities. Thisapplication is not only to search for Dormitory, it canalso place orders online, this is also an opportunity for themanager or owner of the boarding house to promote their Dormitory. Methods of data collection are done by observation, interviews, literature studies, and documentation. The development of this application system uses the Waterfall method. The final result of this research will create an android-based boarding mobile application that can make it easier for boarding house seekers to search and book boarding houses as well as a means of promotion for boarding house owners and managers.

The fear that location privacy may be compromised greatly hinders the development of location-based service. Accordingly, some schemes based on the distributed architecture in peer-to-peer network for location privacy protection are proposed. Most of them assume that mobile terminals are mutually trusted, but this does not conform to realistic scenes, and they cannot make requirements for the level of location privacy protection. Therefore, this paper proposes a scheme for location attribute-based security authentication and private sharing data group, so that they trust each other in peer-to-peer network and the trusted but curious mobile terminal cannot access the initiator's query request. A new identifier is designed to allow mobile terminals to customize the protection strength. In addition, the caching mechanism is introduced considering the cache capacity, and a cache replacement policy based on deep reinforcement learning is proposed to reduce communications with location-based service server for achieving location privacy protection. Experiments show the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

The K -nearest neighbor (K -NN) query is an important query in location-based service (LBS), which can query the nearest k points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of K -NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving K -NN query scheme to address the above two issues. Concretely, our scheme utilizes the K D-tree (K -Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%. [ABSTRACT FROM AUTHOR]