Location-based Services Server Research Articles

A Privacy Protection Method Based on Trajectory Location Point Association

Location-based services (LBS) technology provides personalized services for users. However, private location information faces the risk of being leaked while users are enjoying the LBS services. In order to prevent this from occurring, this study proposes an anonymous privacy protection method based on trajectory location association, abbreviated as AS-AP, which considers the association between the user’s current location and the background information. The association between locations is mined from time and query preferences, and then fake locations and queries are generated as part of the anonymous interference requests. This conforms to the k-anonymity feature, which prevents attackers from inferring the user's real intent from the network information, thereby enhancing the credibility of network technology, and protecting data privacy. The experimental results indicate that AS-AP can provide more personalized privacy protection services while ensuring that users' sensitive data is protected.

SGO: Semantic Group Obfuscation for Location-Based Services in VANETS.

Location privacy is an important parameter to be addressed in the case of vehicular ad hoc networks. Each vehicle frequently communicates with location-based services to find the nearest location of interest. The location messages communicated with the location server may contain sensitive information like vehicle identity, location, direction, and other headings. A Location-Based Services (LBS) server is not a trusted entity; it can interact with an adversary, compromising the location information of vehicles on the road and providing a way for an adversary to extract the future location tracks of a target vehicle. The existing works consider two or three neighboring vehicles as a virtual shadow to conceal location information. However, they did not fully utilize the semantic location information and pseudonym-changing process, which reduces the privacy protection level. Moreover, a lot of dummy location messages are generated that increase overheads in the network. To address these issues, we propose a Semantic Group Obfuscation (SGO) technique that utilizes both location semantics as well as an efficient pseudonym-changing scheme. SGO creates groups of similar status vehicles on the road and selects random position coordinates for communication with the LBS server. It hides the actual location of a target vehicle in a vicinity. The simulation results verify that the proposed scheme SGO improves the anonymization and entropy of vehicles, and it reduces the location traceability and overheads in the network in terms of computation cost and communication cost. The cost of overhead is reduced by 55% to 65% compared with existing schemes. We also formally model and specify SGO using High-Level Petri Nets (HLPNs), which show the correctness and appropriateness of the scheme.

DistPreserv: Maintaining User Distribution for Privacy-Preserving Location-Based Services

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users location distributions by over 90%, while providing high precision and recall of queries.

Drone Helps Privacy: Sky Caching Assisted $k$-Anonymity in Spatial Querying

As wireless and mobile devices are prevalent and resource-rich, users can frequently query point-of-interests and enjoy diverse location-based services (LBSs) in infrastructure-based wireless networks. However, users often trade their location privacy with services by sharing their location information with an LBS server, without knowing that the server can fully be trusted. In this article, we propose a sky caching-aided spatial querying scheme with the support of a flying drone. The basic idea is that both the user and its drone collaboratively generate a set of dummy locations to hide the current location of the user from the server. We also propose cache admission control techniques to efficiently cache the queried results to minimize the number of duplicated cached copies. The location privacy is analyzed and measured in terms of the location obfuscation and the size of the convex hull area that consists of dummy locations resided within the cloaking areas of user and drone. We conduct extensive simulation experiments using the OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed approach can reduce the number of queries sent to the server, extend the cloaking area, and improve the location privacy of users significantly.

A real‐time privacy‐preserving scheme based on grouping queries for continuous location‐based services

SummaryWith the advancement of global positioning systems and communication technologies, location‐based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location‐related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real‐time privacy‐preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure.

Anonymous and Linkable Location-Based Services

In the location-based services (LBS) scheme, the vehicle user sends his query to the LBS server and gets the data corresponding to the requested query from it. Therefore, vehicle user’s query privacy and LBS server’s data privacy are serious issues in LBS. Several LBS schemes, like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, private-information retrieval, and homomorphic encryption-based, have been proposed to solve these issues. These schemes either provide vehicle user’s query privacy or LBS server’s data privacy. Some of the oblivious transfer (OT) protocol-based LBS schemes preserve vehicle users’ query privacy and LBS server’s data privacy. However, the OT-based LBS schemes do not preserve vehicle user’s identity privacy. Moreover, the efficiency (communication and computation costs) of the OT protocol-based LBS schemes depends on the LBS server’s database size, which is very high. In this work, we propose an anonymous linkable location-based services (AL2BS) scheme using linkable ring signcryption technique. The scheme provides vehicle user’s query privacy and LBS server’s data privacy without revealing the vehicle user’s identity. The efficiency of the AL2BS scheme is independent of the size of the LBS server’s database. The AL2BS scheme requires only one pairing operation on both ends and a few point multiplications. The theoretical and experimental results show that the performance of the AL2BS scheme in terms of communication and computation costs is better than the existing LBS schemes.

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks.

Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

Differential Privacy Location Protection Method Based on the Markov Model

Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.

Toward Oblivious Location-Based k-Nearest Neighbor Query in Smart Cities

Enabled by the flourishing Internet-of-Things technology, smart cities can offer a variety of smart services to our daily lives and have received considerable attention in recent years. As a pivotal component of smart cities, location-based services (LBSs) have been deeply exploited by both academia and industry. Meanwhile, since cloud computing can provide reliable and flexible IT resources, many LBS services have been outsourced to the cloud for offering better services. Nevertheless, as the cloud is not fully trusted, privacy preservation becomes an essential requirement for these services. Over the past years, many privacy-preserving location-based k-nearest neighbor ( kNN) query schemes over the cloud have been proposed. However, most of them are subjected to an inevitable design defect, i.e., whenever a user queries twice at the same location, the cloud can identify and return the same query result to the query user, and such information together with third-party data breaches could be exploited by the cloud for some location disclosures. Although some existing schemes can cope with the issue, they are not quite practical, as they will bring heavy overheads on the query user side. In this article, aiming to address the above challenge, we propose a novel oblivious location-based kNN query scheme, in which the cloud cannot link two queries even if they are initiated by query users at the same location. Specifically, based on the modified Paillier cryptosystem, we first present three privacy-preserving protocols, namely, oblivious absolute value calculation, sorting, and top- k extraction. Then, by integrating these three protocols, we propose our novel oblivious location-based kNN query scheme. The detailed security analysis shows that our proposed scheme really enhances the privacy preservation in LBS queries. In addition, extensive performance analysis and experiments are conducted, and the results indicate that our proposed scheme is also efficient for the query user.

Privacy Preserving Location based Services Through K-Anonymized Vehicular Social Network

Location-based services (LBS) are the services that are used through users’ mobile devices and provide them the information regarding nearby restaurants, hospitals, gas stations, shopping malls, cinema (to name a few). While using LBS, a user needs to provide his/her location coordinates (geo-coordinates) to the LBS server. The revelation of a user’s location may seriously jeopardize his/her privacy. A common solution to this problem is the use of an intermediate anonymizer server that obfuscate the real location of a user among k other users. However, in this scenario, the anonymizer server must be a trusted and therefore, inherits the trust related issue and may also become a single point of failure. Moreover, if anonymizer compromises then the privacy of all the users is compromised. A vehicular adhoc network (VSN) is a subset of a mobile adhoc network (MANET) with some unique characteristics such as rapid speed of nodes (vehicles) that quickly change topologies. A vehicular social network is a combination of VANET and online social network. A VSN is formed by likeminded drivers/passengers or by common location. Like mobile nodes, the vehicles also utilize LBS services. However, an anonymizer cannot be used in this scenario because it needs to be updated with current locations of vehicles and therefore, jeopardizes the privacy of vehicles. A VSN can significantly help in this scenario. This research proposes a distributed k-anonymity based scheme that considers the social ties between the users of VSN and enable vehicles to use LBS in a privacy preserving manner. The proposed scheme uses a trusted authority (TA) such as a government law enforcement agency, that registers vehicles. However, the TA does not know what services are requested by a vehicle. On the other hand, the LBS server does not know which vehicle is requesting the service. In the end, the computational and communication overhead of the scheme is presented. The low computational overhead of TA, LBS server and vehicles shows that LBS can serve several vehicles in a very short time. Similarly, the TA can register and provide pseudo-identities to a number of vehicles without any significant delays.

A new grid partitioning technology for location privacy protection

Nowadays, the location-based service (LBS) has become an essential part of convenient service in people's daily life. However, the untrusted LBS servers can store lots of information about the user, such as the user's identity, location, and destination. Then the information can be used as background knowledge and combined with the query frequency of the user to launch the inference attack to obtain user's privacy. In most of the existing schemes, the author considers the algorithm of virtual location selection from the historical location of the user. However, the LBS server can infer the user's location information on the historical data that has been counted for a long time. In order to ensure that the users? historical query data and query frequency will not be obtained by the attacker, we propose a privacy protection algorithm based on grid expansion. With the help of third-party agents, the combination of cooperative users and pseudonyms can resist the privacy disclosure caused by users requesting services during the mobile process. Extensive simulation experiments have been carried out on Gowalla dataset to evaluate the efficiency of the proposed algorithm. By comparing with other existing methods, the experimental verify the effectiveness of our algorithm in privacy protection.

Enhanced peer-to-peer anonymity approach for privacy preserving in location-based services

ABSTRACT 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 could impede the use of this type of services. Current Privacy-preserving techniques mainly preserve location not query privacy (i.e., query issuer identification). The untrusted LBS provider (adversary) can breach user privacy in case that it has some user background knowledge and caches queries from more than one user in the same anonymity region (group). These types of attacks use users’ profiles and cached queries to predict semantically the issuer of each query. In this paper, a peer-to-peer privacy-preserving model is presented to protect the user privacy against these types of attacks taking into account the users’ profiles and cached queries in the LBS server. Using this model, an inference algorithm for predicating semantically the issuer of each query and her/his underlying location is presented to check the probability that a query privacy could be breached. A set of experiments is performed to check the effectiveness of the proposed privacy-preserving model. The results show that the cached queries with semantic matching affect negatively in breaching the query and location privacy.

Location privacy preservation through kernel transformation

SummaryThe 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.

Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services.

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.

Location Privacy-Preserving Method Based on Historical Proximity Location

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.

A k‐nearest neighbor query method based on trust and location privacy protection

AbstractSpatial query is an important supporting technology in the Internet of Things (IoT) and location‐based services (LBS). The k‐nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k‐nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K‐anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user.

An Efficient Approach for Privacy-Preserving of the Client’s Location and Query in M-Business Supplying LBS Services

Privacy-preserving in mobile business supplying location-based services (LBS) has the potential to become a primary concern for clients and service providers. In m-business providing LBS services, a client sends its exact locations to service providers. This data may involve sensitive and private personal information. Therefore, the misuse of location information by service providers creates privacy issues for clients. Moreover, the query must not be linked to the mobile client, even if the location information is exposed willingly by her/him to obtain specific services. Thus, there are location cloaking algorithms that allow the protection of the location privacy of mobile users. Hence, many temporal and spatial approaches to cloaking a specific user’s location have been proposed. Different from the existing methods, the current works define location and query privacy separately. Therefore, in this paper, we investigate the issues related to the mobile client privacy. Mainly, we aim to preserve the client location privacy as well as the continuous queries privacy, where mobile clients continuously emit different queries during their travels. It’s on this premise that we propose a new clique-based cloaking algorithm named Mobile Clique Cloak (MCC) to preserve the mobile client’s privacy in the M-business providing LBS services. Also, to build the cloaking region in our approach, we take into account the similarity of client velocity and direction to obtain a right balance between quality of service QoS and privacy. Furthermore, we generate different realistic dummies instead of dropping the query; thus, all queries will be processed even in the case of k−1 other mobile clients’ queries cannot be found. Moreover, our work deals with a series of attacks in the same cloaking process (location attack, tracking attack, query sampling attacks and homogenous attack). We evaluate our approach from three aspects: privacy guaranty, quality of service and performance. Experimental evaluation of our algorithms on a real world map shows that our approach ensures total privacy for clients and protects the privacy of clients during the entire query period whiles allowing clients’ choice of privacy requirements. Besides, we compare our algorithm with existing privacy protection algorithms such as V-DCA, D-TC and GCA. According to the evaluation results and a comparison of the algorithms, our algorithm MCC can make a good balance between quality of service, performance and privacy.

Smart Privacy Protection for Location-Based Services using Queueing Modelling

In the digital era, we are greatly dependent on the popular applications of the Location Based Services (LBS) in our day-to-day activities. The smart phone comes with a variety of applications which acquire the user location and build up user profile like the user activities, hobbies, places of visit, food orders etc. Such sensitive information in the LBS server can pose privacy risk for the user. To safe guard the user from such threat we propose a smart privacy protection technique in this paper that can conceal the user location when using the location based services. We adopt the generation of dummy locations to obfuscate the user original location from the LBS server. The server generates the result set for the dummy user locations. In this work we try to optimize the things at server as well as user ends with two objectives. The first goal is to work towards identifying the overlap in result sets and generate unique and reduced result set with which the communication load on the network can be reduced. The second goal is to prioritize the result set by Queuing model for the result set through which waiting time of the customer can be minimized. We have also illustrated that this model show good performance in terms of the reduced communication load through experimental results.