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With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this paper, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this paper, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption and linear encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy of mobile user. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

In internet of vehicles (IoV), the Vehicle ad-hoc network(VANET) provides the location based service(LBS) when vehicles communicate with the dynamic environment. As an integration of satellite systems and terrestrial communications, the space-air-ground integrated network(SAGIN) provides a reliable and efficient way for LBS. But the privacy protection in SAGIN cannot meet LBS security requirements well, so we present a blockchain-based LBS security preserving trust model. K-anonymous location privacy protection algorithm is used to hide users’ real position, so that users can avoid personal privacy disclosure when requesting location-based services. We propose a trust management algorithm which can detect the malicious behaviors when constructing anonymous regions and clear the malicious users out of the system. Besides, we use blockchain to implement the transparency and conditional anonymity of the system. Missive experiments indicate that our scheme is feasible and outperforms part of state-of-the-art privacy protection approaches.

With the advancement of mobile internet, cloud computing, and smart sensing devices, Location-Based Services (LBSs) have become more and more indispensable in the Internet of Things (IoT) based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This paper proposes two Secure Dynamic Spatial Keyword Queries (SDSKQ) constructions that support expressive query types and dynamic update. Firstly, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure for spatio-textual data, named Encrypted Textual Signature Quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world datasets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.

With the popularization of the geo-positioning technologies and Location Based Service (LBS), the spatial range query has gained increasing attention in VANET. To eliminate local data storage and computation burden, the data owner outsources his/her spatial data and the processing of range query tasks to location based service providers. However, outsourcing tasks may bring the risk of privacy leakage as location based service providers and clients, including data owner and data users are not in the same trusted domain. Besides, malicious service providers may forge or tamper retrieval results due to interest incentives such as saving storage or computational capacity. Therefore, we first propose and construct the Verifiable Spatial Range Query (VSRQ) scheme in VANET by using the hierarchical cell based encoding method, G-tree and the accumulator. To guarantee the correctness and completeness of search results, we leverage an accumulator-based technology. Then, we develop an improved G-tree to accelerate query processing and extend it to support multi-dimensional range queries. Security analysis shows that basic VSRQ and extension of VSRQ schemes are secure in terms of the privacy of index and confidentiality of spatial data. Extensive experiments further show that, basic (or extension of) VSRQ scheme can achieve result integrity while supporting spatial range query and lower computational and storage burden compared with existing schemes.

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.

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

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.

With the maturity of Internet of Things technology, location-based service (LBS) is developing rapidly in intelligent terminal devices, and it brings new vitality to the fields of logistics, transportation, product traceability and so on. The popularity of LBS produces a lot of spatial data, which inevitablybrings burden to the storage and management of LBS provider (LBSP). With the help of cloud computing and cloud storage, outsourcing spatial data to cloud server has become a new trend. However, due to the cloud server is not trusted, data outsourcing will face the problems of data disclosure and query disclosure. Range query is a common query in LBS, considering the situation of data outsourcing, this paper proposes an accurate range query (ARQ) scheme, which can realize efficient range query while preserving LBSP’s data privacy and user’s query privacy from being disclosed to the cloud server. The ARQ scheme is suitable for spatial data in any form without being limited to the case that the data points are only integers, which has acertain practical significance. In addition, by dividing the region into atomic regions, ARQ can realize sub-linear search time and ensure dynamic update of spatial data. We proved the security of the proposed scheme through security analysis, and demonstrated the effectiveness of the scheme through experiments.

Urban green spaces (UGSs) can provide important ecosystem services for citizens and their well-being. To make use of these services according to UGS user demands, urban residents, tourists, and city administrations should know where UGSs are located, what qualities they have and how to reach them on convenient routes. This paper presents a novel digital infrastructure which combines and fuses different data to map UGSs and their qualities, and makes this information available in a web app. The interactive information service of the app aims to support citizens to explore and search for suitable UGSs and to provide routing options to reach them based on their preferences. Via implicit and explicit feedback functions included in the app, further information on UGS users' preferences can be collected to enhance the overall knowledge basis, while respecting data privacy issues. The underlying data base consists primarily of open and volunteered geographic data, which allows for transferability to other cities. This paper describes the system design, its back-end and front-end components, as well as the process of development and deployment of the system in two pilot cities. Preliminary results of the piloting in the two cities are presented, focusing on user preferences for UGSs searches. The added value of the web app for city residents and the role of the newly gained knowledge for urban planning is discussed and reflected upon.

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and office), which we call it \textit{spatiotemporal event}. In this paper, we first formally define {spatiotemporal event} as Boolean expressions between location and time predicates, and then we define $ \epsilon $-\textit{spatiotemporal event privacy} by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user's initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with \textit{any} prior knowledge. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient., Comment: accepted in TKDE. arXiv admin note: substantial text overlap with arXiv:1810.09152

—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.

Technological developments rapidly and affect of changes in human lifestyle. The demand of it is increasingly sophisticated technology that cause humans create tools that facilitate human work. One of them is searching location by using GPS (Global Positioning System). It determines the user's location by using Location Based Service. It is expected to overcome the problem of user in searching location. This paper discusses the user searches location by using Location Based Service. Through the help of Location Based Service will be pinned and will help to determine where the location of a user to their android based smartphones. The results can be obtained from the User Acceptance Test (UAT). It has a percentage of 80% of the 20 users performed. Furthermore, it is not appropriate to have a percentage of 20% of 20 peers. Keywords:Location Based Services, Global Positioning System (GPS) Android

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.

User location information contributes to in-depth social network data analytics. This special issue focuses on emerging techniques and trendy applications of real-time dynamic network learning in the fields such as neural network, dynamic network, spatial feature pattern recognition, and active learning. The research included at this special issue will also advance the location based services in real applications.

The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.

Preserving user privacy and authenticity are essential requirements for location based services in order to protect user’s confidential information from public exposure and provide secure access to various services. Recently, numerous approaches towards these challenges have been proposed. Many of these are based on dynamic update of fixed parameters (such as pseudonym, transaction sequence number, shared key, counter, etc.) along with symmetric/asymmetric key cryptography, and seems promising in dealing with various security related issues such as unlinkability, forward/backward secrecy, replay attack and stolen verifier attack. However, the concept of dynamic update may affect the system performance in case of desynchronization attack as it requires to perform additional computations or user reregistration in order to resynchronize the peers. In this article, we address the problem of desynchronization attack and propose a privacy preserving user authentication protocol for location based services. The proposed protocol is based on elliptic curve cryptography and introduces dynamic randomized counters in order to synchronize the peers. Also, there is no need to resynchronize the peers in case of desynchronization attack. Additionally, there is no timestamp used in construction of the protocol to avoid clock synchronization problem. The security properties of the protocol are validated both formally and informally. Moreover, the safety of the protocol is assured using AVISPA tool based automated simulation. Finally, a performance comparison has been made against some recently proposed approaches to ensure the effectiveness of our protocol in real life implementations.

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user’s location. Hence, it will pose a huge threat to user’s privacy if the sensing locations are published without protection. As usual, the user’s location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user’s sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user’s location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism.

Vehicular ad hoc network(VANET) is a special mobile ad hoc network (MANET) which plays an important role in the intelligent traffic system(ITS). Based on the high mobility of VANETs, the security problems have not been reasonably solved when we enjoy the convenience brought by the Location Based Service(LBS). We present a blockchain-based trust management model for location privacy preserving. The scheme allows vehicles to use certificate to request LBS without revealing their privacy information. We construct anonymous cloaking region to ensure the privacy security of vehicles. We propose a trust management algorithm to constrain and standardize the behavior of vehicles, and use blockchain to implement the data security of vehicles. In the experiments, we conduct the tests with various data sets. Security analysis and experiments show that the system is resilient to sorts of trust model attacks, which can better preserve the privacy security of vehicles. Simulation results reveal that the proposed system is effective and feasible in collect.

With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) are becoming more and more popular. However, for the users being served, how to protect their location privacy has become an increasingly concerned issue. This has led to great difficulty to establish trust between the users and the service providers, hindering the development of LBS for more comprehensive functions. In this paper, we first establish a strong identity verification mechanism to ensure the authentication security of the system, and then design a new location privacy protection mechanism based on the privacy proximity test problem. This mechanism not only guarantees the confidentiality of the user’s information during the subsequent information interaction and dynamic data transmission, but also meets the service provider’s requirements for related data.

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

MAR (Mobile Augmented Reality) is delegated an innovation that gives similar capacities as AR (Augmented Reality), yet without the actual limitations of the area of an exploration office or testing region. A Location-Based Service (LBS) is an application for portable figuring which gives clients administrations dependent on their topographical area. Area based administrations are getting progressively famous with the ascent of cell phones with an ever increasing number of highlights (particularly Apple's iPhone and Android-based gadgets). Increasingly more substance is improved with geo-information and can subsequently be seen in a virtual climate, however in real, portable conditions and in a setting delicate way fit to the requirements of the client. The definition and advantages of versatile increased reality and area based administrations and the mix of portable enlarged reality and area based administrations are broke down in this article. The issues are examined alongside the upsides and downsides.

The internet of things provides a convenient life for all of us. Physical devices with locational systems connected to the internet can provide navigation and recommendation services in cooperation with the Location Based Services (LBS). The nearest neighbor query is the most common query type in the LBS research area. On the other hand, due to privacy protection or inaccuracies in measurement instructions, data uncertainties are typical in the IoT environment. This uncertainty will affect the results of nearest neighbor queries. The existence of plane obstacles also brings the challenge to the spatial data query. In this paper, the obstacles and data uncertainties in continuous nearest query are studied. We also designed a pruning strategy that significantly reduced the number of objects to be calculated. In addition, we propose a concept of safe region and an algorithm to generate a safe region. We also designed an index method for saving safe regions. The experimental results show that our approach outperforms the state-of-the-art one in efficiency and scalability.

Location-based services are one of the most important services offered by mobile social networks. Offering this kind of service requires accessing the physical position of users together with the access authorizations, i.e., who is authorized to access what information. However, these physical positions and authorizations are sensitive information that have to be kept secret from any adversary, including the service providers. As far as we know, the problem of offering location-based services in mobile social networks with a revocation feature under collusion assumption, i.e., an adversary colludes with the service provider, has not been studied. In this article, we show how to solve this problem in the example of range queries. Specifically, we guarantee any adversary, including the service provider, is not able to learn (1) the physical position of the users, (2) the distance between his position and that of the users, and (3) whether two users are allowed to learn the distance between them. We propose two approaches, namely, two-layer symmetric encryption and two-layer attribute-based encryption. The main difference between them is that they use, among other encryption schemes, symmetric and attribute-based encryption, respectively. Next, we prove the secrecy guarantees of both approaches, analyze their complexity, and provide experiments to evaluate their performance in practice.

In vehicular social networks (VSNs), edge stations or cloud service provider can support the traffic services or location services to vehicles. Moreover, the information sharing among vehicles can assist vehicles to avoid traffic accidents and guarantee driving safety. However, it is easy to be threatened in the communication of vehicle-to-edge station, vehicle-to-cloud service provider and vehicle-to-vehicle in VSNs, which leads to the vehicle’s privacy leakage easily. Besides, some malicious users may provide untrustworthy information to mislead others just for its selfishness. Hence, in this paper, we propose an efficient, reliable and privacy-preserving scheme based on blockchain for VSNs. In our scheme, pseudonym mechanism is employed to achieve individual anonymization by concealing the vehicles’ identity. Moreover, to encourage vehicles to report trustworthy information, incentive-punishment mechanism is proposed. Meanwhile, we propose multi-factors and single-factor weight-based evaluation mechanism to evaluate the reliability of message. Additionally, Practical Byzantine Fault Tolerance (PBFT) and blockchain are also employed to achieve consensus and store the records respectively, which can prevent malicious entities from manipulating on vehicles’ reward scores and credit scores. Finally, we analyze the security of the proposed scheme in terms of external attacks, internal attacks, collusion attacks etc. The relevant experimental results are shown that our scheme is feasible and efficient.

Geo-textual data is ubiquitous nowadays, where each object has a location and is associated with some keywords. Many types of queries based on geo-textual data, termed as spatial keyword queries, have been proposed, and are to find optimal object(s) in terms of both its (their) location(s) and keywords. In this paper, we propose a new type of query called nearby-fit spatial keyword query (NSKQ), where an optimal object is defined based not only on the location and the keywords of the object itself, but also on those of the objects nearby. For example, in an application of finding a hotel, not only the location of a hotel but also the objects near the hotel (e.g., shopping malls, restaurants, and bus stops nearby) might need to be taken into consideration. The query is proved to be NP-hard, and in order to perform the query efficiently, we developed two approximate algorithms with small constant approximation factors equal to 1.155 and 1.79. We conducted extensive experiments based on both real and synthetic datasets, which verified our algorithms. Nanyang Technological University Accepted version The research of Cheng Long is partially supported by NTU SUG M4082302.020.

With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.

© 2012 IEEE. With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users' location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don't provide differentiated protection for users' different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users' personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users' personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user's service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.

Nowadays, Location-based services (LBS) are important services that take benefits from the revolution in communications. Nevertheless, user’s privacy is considered as significant challenge that cou...

Ubiquitous smartphones with their built-in location services enable people to explore various points-of-interest (POIs) through location-based apps, e.g., Yelp and Foursquare City Guide. With these apps, users can receive personalized recommendations on nearby places, e.g., restaurants and arcades, which not only saves them searching time, but also helps find POIs that are of interest to them. One issue with these apps and almost all existing recommender systems is that they require users to share their preference data with the service providers. This information, if not properly used, can leak users’ privacy. In this paper, we propose a group preference-based POI recommendation scheme which fuses matrix factorization and clustering techniques to provide quality recommendations without sacrificing users’ privacy.

Location-based service (LBS) provides more and more conveniences to people. However, it also brings potential threats of offending users’ privacy. How to protect users’ privacy in LBS schemes has aroused increasing research interests in recent years. Most of the existing privacy-preserving LBS schemes are based on the hardness of traditional number-theoretic problems such as the integer factorization or the discrete logarithm problems. However, with the development of large scale quantum computers, these traditional problems can be easily solved by Shor's algorithms, hence the security of these LBS schemes is greatly threatened. In this paper, we solve this problem by constructing a privacy-preserving LBS scheme against quantum attacks from an LWE-based key-homomorphic pseudorandom functions (PRF). In our scheme, due to the key-homomorphic property of the PRF, an LBS user only has to compute one PRF value of the target location and the remaining computation is outsourced to a cloud server, which releases the user from heavy computation burden. In addition, by dividing the key encrypting LBS data into two parts and assigning the two parts to the cloud sever and each user respectively, our scheme avoids the threats of key abuse and information leaking of LBS data. Moreover, we use this PRF to realize an authenticated protocol, which protects the communications between the LBS users and the cloud server. We stress that the security of our scheme is based only on the security of the LWE-based key-homomorphic PRF, hence our scheme is the first LBS scheme secure against quantum attacks.

Road network functional hierarchy classifies individual roads into several levels, for efficient traffic management and road network generalization purposes. Automatic and semi-automatic road network extraction methods exist, but the generated products normally lack information on its functional hierarchy. This paper presents a methodology for automatically retrieve functional hierarchy for an OpenStreetMap derived road network from Floating Car Data, obtaining evenly distributed (e.g. for generalization purposes) or dynamic (e.g. to take into account differences in traffic volumes in different moments of the day) classifications. Road network elements are classified in function of vehicle speed values: the class distribution generated with the proposed methodology follows a linear distribution that can be better exploited for generalization purposes. Furthermore, the methodology allows to clearly distinguish different distributions in different moments of the day and days of the week, supporting traffic management activities.

In recent years, with the development of technologies such as the Internet, sensors and RFID, the application of the Internet of Things in various industries has deepened, resulting in a large number of new technologies, new products and new applications. There are a large number of devices in the Internet of Things. The interconnection of location information of these devices brings convenience to people’s lives, and there is also the problem of leaking location privacy. Therefore, effective management of the location information of the device is crucial to the development of the Internet of Things. At present, in the aspect of device location information supervision, the traditional encryption method or the location information is generally blurred, which improves the security of the location information, but also reduces the applicability of the location information. Aiming at these problems, based on the research on the equipment positioning of the Internet of Things and the key technologies of the blockchain, this paper designs a location chain storage system for IoT devices based on blockchain, and experiments the system. The results show that the number of transactions in the block has a significant impact on the time the system provides location services. The number of transactions in the block can be reasonably set by calculating the network nodes in the blockchain. The results show that the system can protect the device. Providing location information services to users under the premise of location privacy.

Advances in technology in the field of Android-based applications are expected to bring changes in the world of education intended for English language education. At present the method of searching institute courses that are present in a variety of applications offered, but the existing applications are not yet complete on this matter related to the applications offered do not meet the specific needs of the course institution search applications that are provided in the English village pare. LBS and algotima dijkstra can be used in the analysis and design of android-based applications, it is required an application to search for locations such as applications needed by user in addition to places of study can also find a place to spend the night while studying, where traveling provides the facilities that have been provided. The results of the Go-Course application can conclude that it must meet the needs of users to carry out the process of finding a chair, boarding house, boarding house and travel package from a smartphone by providing a fairly accurate position. By applying the Djikstra algorithm to the Go-Course Application, you can set the shortest route to the boarding house and boarding house with the user's position and current distance in the form of a map. All functions in the Go-Cource application for finding seats, boarding, boarding and tour packages can run correctly through Blackbox Testing and in accordance with manufacturing recommendations.Keywords: LBS, Dijkstra, Android, GPS, Go-Course.

People spend most of their life time in indoor environments and in all of these environments, Location Service Providers (LSPs) improve users’ navigation. Preserving privacy in Location Based Services (LBSs) is vital for indoor LBSs and fingerprinting based indoor localization method is an emerging technique in indoor localization. In such systems, LSP may be curious and untrusted. Therefore, it is preferred that user estimates its location by using a Partial Radio Map (PRM) which is achieved by LSP, anonymously. In this paper, a privacy preserving method that uses Bloom filter for preserving anonymity and creating PRM during localization process, is proposed. In this method, LSP cannot recognize user identity, which is anonymized by the anonymizer. The proposed method has lower computational complexity compared with methods that use encryption or clustering concepts. The proposed method also has higher accuracy in localization compared with those that use Bloom filter with one random selected AP. Then, in order to decrease the complexity and to increase the accuracy at the same time, we introduce a method that expands the radio map by authenticated users, without compromising their privacy. We also enhance the performance of this method, using Hilbert curve for preserving the ambiguity of users’ location. After verifying the user’s data, LSP sends a certificate to the authenticated users. This certificate can increase the priority of users in LBS requests. Simulation results and measurements show that the proposed method on average improves the localization accuracy up to 16% compared with existing location privacy methods.

Blockchain can be utilized to enhance both security and efficiency for location-based service (LBS) in vehicular social networks (VSNs), due to its inherent decentralization, anonymity, and trust properties. Unfortunately, the existing approaches either lack effective authentication, which is vulnerable to the man-in-the-middle attack, or require an online certificate authority (CA) where frequent interactions with resource-constrained vehicles are required. To address these challenging issues, in this article, a lightweight threshold CA for consortium blockchain along with a privacy-preserving LBS protocol in blockchain enforced VSNs is proposed. First, a lightweight threshold CA framework LTCA is proposed by devising a threshold proxy signature, where the proxy signing key is issued by a coalition of threshold number of CAs playing the roles of authorized nodes in the consortium blockchain. Without the intervene of an online CA, each vehicle in the online phase can authenticate its identity by itself each time its blockchain address (i.e., account address) is updated. Then, based on the proposed LTCA, an efficient privacy-preserving LBS protocol PPVC is contrived to protect each vehicle’s conditional identity privacy with a moderate cost. Finally, both security analysis and performance evaluation demonstrate the effectiveness and efficiency of our proposed LTCA and PPVC in blockchain enforced VSNs.

Location-based services (LBSs) have increasingly provided by a broad range of devices and applications, but one associated risk is location disclosure. To solve this problem, a commonly method is to adopt K-anonymity in the centralized architecture based on a single trusted anonymizer. However, this strategy may compromise user privacy involving continuous LBSs. In this study, we propose a dual-K mechanism (DKM) to protect the users’ trajectory privacy for continuous LBSs. The proposed DKM method firstly inserted multiple anonymizers between the user and the location service provider (LSP), and K query locations are sent to different anonymizers to achieve K-anonymity. Simultaneously, we combined the dynamic pseudonym and the location selection mechanisms to improve user trajectory privacy. Hence, neither the LSP nor the anonymizer can obtain the user trajectory. Security analyses demonstrates that our proposed scheme can effectively enhance user trajectory privacy protection, and the simulation results prove that the DKM scheme can preserve user trajectory privacy with low overhead on a single anonymizer.

Mobile devices, especially the Android operating system, are very easy to use to provide information related to work or location precisely and accurately, especially searching for the location of facilities in DKI Jakarta, such as hospitals, fire stations, restaurants, hotels, and other places. But so far the problem is often encountered related to inaccurate information reports where reports take the form of images and text without a real location statement. For this reason, it is necessary to design a mobile application that can provide detailed information about the location of several public facilities in DKI Jakarta using the Software Development Life Cycle (SDLC) method. Applications that are made can provide information that has several features including image information, title, description, location, and weather.

Location-based services have attracted much attention in both academia and industry. However, protecting user’s privacy while providing accurate service for users remains challenging. In most of the existing research works, a semi-trusted proxy is employed to act on behalf of a user to minimize the computation and communication costs of the user. However, user privacy, e.g. location privacy, cannot be protected against the proxy. In this paper, we design a new blind filter protocol where a user can employ a semi-trusted proxy to determine whether a point of interest is within a circular area centered at the user’s location. During the protocol, neither the proxy nor the location-based service provider can obtain the location of the user and the query results. Moreover, each type of query is controlled by an access tree and only the users whose attributes satisfy this access tree can complete the specific type of query. Security analysis and efficiency experiments validate that the proposed protocol is secure and efficient in terms of the computation and communication overhead.

PurposeLocation-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.Design/methodology/approachThe industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.FindingsThe coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.Research limitations/implicationsThis study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.Originality/valueAn industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

In Service Computing, Service selection plays a vital role in delivering an appropriate service to the end user based on the request. Service composition methodology is the major factor affecting the appropriate need for the user or the consumer. The proposed technique involve the use of hybrid meta heuristics genetic algorithm with tabu search to retrieve the best suitable web service to the end user based on the Quality of service parameters. The existing techniques use the parameters response time, cost and reliability. The technique used in hybrid algorithm is used computes the availability of service, response time, throughput and interoperability between the services. Hence the result of service composition gives high reliable service to the end user with maximum throughput and interoperability. The location based service selection mechanism is considered for service composition as almost 90% of the services are available in cloud.

Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users’ location privacy, due to its more and more untrusted server-side. In this article, we propose a location privacy-preserving system for LBS by constructing “cover-up ranges” to protect the query ranges associated with a location query sequence. Firstly, we present a client-based system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users’ location privacy on the untrusted server-side, without compromising the accuracy and usability of LBS.

Geospatial location matching plays a significant role in spatial databases. In this paper, we propose and study a novel parallel spatio-textual location matching (STLM) query. Given two sets P and Q of spatial locations with textual attributes, a spatio-textual matching threshold $$\theta $$ , the STLM query finds all location pairs whose spatio-textual similarity exceeds $$\theta $$ . We believe that the STLM query is useful in many applications such as important location/hot region detection, duplicate spatio-textual data cleaning, and location based services in general. The STLM query is challenging due to three reasons: (1) how to evaluate the spatio-textual similarity between two locations practically, (2) how to prune the search space effectively in both spatial and textual domains, and (3) how to process the STLM query in parallel because of its high computation complexity. To overcome the challenges, we develop a novel direct matching (DM) search algorithm. A linear combination method is adopted to combine the spatial proximity and textual similarity together. To further improve the query efficiency, we develop a grid-based expansion scheduling scheme based on a purposeful grid index structure. We conduct extensive experiments on real and synthetic spatio-textual data sets to verify the performance of the developed algorithms.

As a primitive function of location-based services (LBSs), the location sharing aims to provide a user's current location information to other designated users. In recent years, LBSs have become one of the most popular services provided by mobile online social networks (mOSNs). As LBSs actively exploit the users’ identity and current location information, appropriate approaches have to be utilized to protect the location privacy of the users. Several recent reports have discussed the significance of friendship privacy protection with the goal of hiding the friendship relation of users from unintended entities. However, to the best of our knowledge, there hasn't been an approach for protecting the location sharing with complete privacy of location and friendship connections. To address this issue, we propose a new cryptographic primitive, functional pseudonym, for location sharing in mOSNs that ensures both of them. Unlike many of the existing solutions, our approach does not require a fully trusted server and does not assume pre-established secrets among friends, and therefore is highly practical. Also, the proposed approach significantly reduces computational overhead of users by delegating part of the computations for location sharing to a server, therefore it is highly sustainable. Our primitive can be widely used in many mOSNs to enable LBSs with improved privacy and sustainability. Consequently, it will contribute to proliferate LBSs by eliminating users privacy concerns.

With the development of smartphones and mobile wireless network, social discovery applications are becoming more and more popular now. These applications help users to chat with nearby people and m...

Trans Jogja merupakan sistem transportasi bus yang melayani rute perjalanan seputar Kota Yogyakarta. Sistem ini mulai dioperasikan pada awal maret 2008 oleh Dinas Perhubungan Pemerintah Provinsi Daerah Istimewa Yogyakarta. Industri transportasi untuk saat ini mengalami perkembangan pesat terutama pelayanan tehadap penumpangnya. Namun pada penerapan sistem ini muncul permasalahan seringnya keterlambatan kedatangan armada bus serta penumpang tidak mengetahui dimana saja letak halte. Dari permasalahan diatas, peneliti membuat sebuah aplikasi yang dapat memudahkan para penumpang. Dengan perkembangan teknologi Location Based Service yang mampu memberikan layanan untuk mengetahui keberadaan dari suatu armada bus maupun halte. Aplikasi ini terintegrasi dengan layanan Google Maps untuk mengetahui antara pengguna dengan posisi armada bus dan halte. Untuk mengetahui posisi pengguna secara realtime dengan memanfaatkan teknologi Global Positioning System yang ada pada smartphone android. Abstract Trans Jogja is a bus transportation system that serves the route around Yogyakarta city. This system began to be operated in early March 2008 by the Provincial Government Transportation Department of Yogyakarta Special Region. The transportation industry for now is experiencing rapid development especially the service to passengers. However, in the implementation of this system emerged the problem of frequent delay in the arrival of bus fleet and passengers do not know where the location of the bus stop. From the above problems, researchers create an application that can facilitate the passengers. With the development of Location Based Service technology that is able to provide services to know the existence of a bus fleet or bus stop. This app integrates with the Google Maps service to know between users with bus fleet positions and bus stops. To know the position of users in realtime by utilizing the technology of Global Positioning System that exist on android smartphone.

While enjoying the convenience brought by Location Based Service (LBS), the location privacy of vehicles in VANET may be disclosed. Distributed k -anonymity, as one of the most popular privacy protection methods, fails to take the trustworthiness of participants into account, resulting in malicious tracing of vehicles, which further leads to the sensitive information leakage, and even the safety threat of personal property. To address this issue, we propose a blockchain enabled trust-based location privacy protection scheme in VANET. Specifically, by analyzing the different requirements of the request vehicle and the cooperative vehicle during the process of constructing the anonymous cloaking region, as well as combining the characteristics of these two roles, we devise the trust management method based on Dirichlet distribution , such that both the requester and the cooperator will only cooperate with the vehicles they trust. Moreover, by employing blockchain, we also proposed the data structure to record the trustworthiness of vehicles on publicly available blocks timely, so that any vehicle can access the historical trust information of counterparties whenever necessary. Finally, the construction process of anonymous cloaking region is presented. Security analysis and extensive experiments indicate that the proposal is resilient to various trust model attacks, it can effectively detect the malicious vehicles, and preserve the location privacy of vehicles in the anonymous cloaking region construction, while the required time delay is limited.