An Authentication Approach for Multiple-User Location-Based Queries

The emerging new services for GPS and mobile users have developed applications that access and exploit spatial objects with new components (e.g. text and social network). Web objects, including blogs, tweets, photos and videos, are embedded into a map by the APIs of map service providers, where textual messages are associated with geographic information. Location-based social networking services, arising from Facebook and Foursquare, allow users to browse and share their traces of locations among the social networks. Among this class of applications, the highlight is that the retrieved spatial objects (e.g. points of interest and moving users) are featured with new components. Integrating such new components into the spatial query processing has produced large amounts of promising results. However, handling new components along with the retrieval of spatial objects increases the complexity of such joint query processing significantly. Thus, management over data from such multiple domains has been received considerable attention from database research community.\n\nIn this thesis, we introduce three interesting problems and study their sophisticated solutions for processing spatial objects with new components: (i) category-aware optimal route query (CORQ), (ii) social and spatial ranking query (SSRQ), and (iii) efficient notification of meeting point (ENMP) query. Our results for (i) and (ii) facilitate the retrieval of spatial objects from multiple domains while our solutions for (iii) provide effective tools for synchronous management of multiple moving users from a social network. Category-aware optimal route queries (CORQ) are generalized from the traveling salesman problem and enable users to retrieve shortest routes covering selected categories. Social and spatial ranking queries (SSRQ) are relevant to spatial object recommendations using social information and allow users to obtain the spatial objects that not only are near their locations but also impress them with high social influence. Efficient notification of meeting point (ENMP) queries are variants of aggregate nearest neighbor queries and provide real-time rearrangement for multiple moving users according to their locations.\n\nQuery processing in such multiple domains is complicated due to the mixture of domain information and their integration within one search. Naïve algorithms for these problems incur either numerous expensive evaluations or massive communication cost, which render them inapplicable to large datasets. Our main research purpose is to design efficient and effective solutions for the proposed problems, that avoid the aforementioned shortcomings of naïve algorithms.

This paper introduce a geographic information is present on the internet which is in the scattered format. When user search for any place then various locations based service providers find out the results. The commonly used location based service providers are Google, Bing, Yahoo etc. we considers a novel distributed system for collaborative location-based information generation and sharing which become increasingly popular due to the explosive growth of Internet-capable and location-aware mobile devices. The results that are given by location based service providers not fully processed. The system consists of a data collector, data contributors, location-based service providers (LBSPs), and system users. The data collector gathers reviews about points-of-interest (POIs) from data contributors, while LBSPs purchase POI data sets from the data collector and allow users to perform spatial zonal top-k queries which ask for the POIs in a certain region and with the highest k ratings for an interested POI attribute. The data contributor is the peoples or users who provide the information about the place. The user can gives comment and reviews to the product or the place. It's not necessary the LBSP provides proper information. In practice, LBSPs are untrusted and may return fake query results for various bad motives, e.g., in favour of POIs willing to pay. In our approach when the data contributor searches for any query, it will process by the LBSP. But before result showing to the user, it will process by the data collector. There are various results for the same query. So each query processed and special top result according to the location will provide to the users. This paper presents three novel schemes for users to detect fake spatial snapshot and arranging top-k query results as an effort to foster the practical deployment and use of the proposed system.

This paper introduces results of two surveys focused on the use of smart mobile devices within the higher education. Data were collected by the method of questionnaire in the research sample of 205 students of the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, matriculated in IT and Management study programmes in 2013/14 and 2015/16 academic years.

Location privacy: The challenges of mobile service devices

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and Internet of thing (IoT) devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. Theoretical proofs of the convergence of quantization factors, unbiasedness of FTTQ, as well as a reduced weight divergence are given. On the basis of FTTQ, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available data sets, and our results demonstrate that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data in contrast to the canonical federated learning algorithms.

Background and Purpose: In recent years, the use of smart mobile devices due to easy access, portability, and also simpler user interface, compared to desktop computers has been met with unparalleled acceptance by Iranian users, managers, and sellers of goods. That's why application development for smart devices in Internet businesses has become essential. On the other hand, the location-awareness of such programs will make it smarter and provide targeted advertising and services to users.Methods: This study provides a model that first handles and categorizes information from some service centers and quickly exchanges data between smart media (such as a mobile, tablet) and data centers based upon online map services (Google Maps). Second, based on the components of location-based services (LBS), in order to provide effective advertising to users, the components of selecting the service center and advertising and navigation of the user to it were put on the agenda based on a loose coupling strategy.Findings: In order to have the features of recommendation, three criteria of desirability, namely the introduction of the final option based on the shortest route in the transportation network, or based on user scores or based on scores allocated by the relevant service unit class were considered. Finally, the Location Finder application was implemented under the Android operating system using the new technologies of the mobile and web-based spatial information system through the proposed model.Discussion and Conclusion: The results of the questionnaire were statistically analyzed using the frequency of the respondents and significantly showed the users' satisfaction with the program, with 55 percent rating it as a great choice for quality, and its compliance with the set goals so that it could fill part of the gap caused by the advertising software.

POIs (points of interest) as well as users’ check-in information and their ratings on POIs have been widely studies in systems providing location-based services. We note that users usually have their own preferences for POI categories and their own network of friends. Therefore, we aim to provide for a group of users a new kind of POI-finding query that considers not only POI preferences of each user but also other aspects of location-based social networks such as users’ locations and POI ratings. We name such a new query as Spatial Group Preference (SGP) query. For a group of users, an SGP query returns top-k POIs that are much likely to satisfy the needs of users. Specially, we propose a new evaluation model that considers user preferences for user preferences for POI categories. Based on this model, we develop basic algorithms based on R-tree to evaluate SGP queries. Further, we design a new index structure called CR-tree to accelerate the query performance. We prove that CR-tree has better pruning efficiency than the traditional R-tree. We conduct experiments on a simulation dataset as well as two real datasets with respect to various configurations. The results suggest the efficiency of our proposal.

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

As use of location-based services (LBSs) is becoming increasingly prevalent, mobile users are more and more enticed to reveal their locations, which may be exploited by attackers to infer the points of interest (POIs) the users visit and then their privacy information. We propose a novel approach to the protection of a user's location privacy based on unobservability, preventing the attackers from relating any particular POI to the user's current location. We design, implement, and evaluate a privacy-protection system, called the Location Information ScrAmbler (LISA) which protects the user's location privacy by adjusting the location noise and hence, the uncertainty of associating his location with any POI, while conserving resources (especially battery energy) on mobile devices. By protecting location privacy locally on each mobile user's device, LISA eliminates the reliance on the trusted third-party servers required by most existing approaches. Therefore, it not only avoids the vulnerability of a single point of failure, but also facilitates the deployment of LBSs. Our evaluation of LISA using real-world users' traces demonstrates its efficacy and efficiency.

SummaryIn recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

In database outsourcing, the data owner delegates the tasks of data management to a third-party service provider. As the service provider may be untrusted or susceptible to attacks, query authentication is an essential part. Merkle R-tree (MR-tree) is one of the most efficient authenticated index that combines Merkle hash tree with R*-tree. MR-tree can provide an efficient range query authentication, however, as it uses the traditional R*-tree query structure in neighbor queries, a large number of unnecessary nodes may be accessed, and that can affect the efficiency of the query. In this paper, the neighbor relationship is introduced into the construction of MR-tree, and we propose a new index structure, called VMR-tree that incorporates the Voronoi diagram into MR-tree. In order to utilize VMR-tree index structure, we propose algorithms for spatial nearest neighbor queries and experiments to verify it has a better efficiency in spatial neighbor query authentication.

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

In the Internet of thing (IoT), with the geographic location of geospatial sensor data and the global positioning systems, location-based services (LBSs) can provide powerful location-aware IoT applications for mobile clients according to their current locations. For LBSs, a k-nearest neighbor (kNN) search can provide a mobile client with geospatial sensor data of k-nearest spatial points of interest (POIs) according to its current location. In this paper, we propose a spatial air index with neighbor information to organize IoT geospatial sensor data for processing kNN searches in the wireless broadcast systems. Since the answered POIs may be neighbors of each other, we add neighbor information to the index structure, which is interleaved with geospatial sensor data, to speed up the query processing. To avoid unnecessary examination of geospatial sensor data from the wireless channel, the proposed method provides the centroid of geospatial data and the corresponding longest distance between the centroid and geospatial data in the region. With this information, the query processing of a kNN search can quickly determine whether to skip examining this region, saving energy consumption of the mobile device. Performance evaluations have verified that the proposed method outperforms the distributed spatial index.

The increase in use of Smart mobile devices has allowed for an ever growing market for services providers. These services are increasingly used to connect to users' on-line private information through social networking sites that share and personalise on-line information. This leads to the problem of privacy leaks stemming from an application's non-adherence to a predefined set of privacy policies. This paper presents a formal methods tool to reliably restrict the access to content in on-line social network services. The Poporo tool builds upon a previous work in which we provided a predicate calculus definition for social networking in B that models social-network content, privacy policies, and social-network friendship relations. This paper presents the implementation and the functionality of our Poporo tool through a running example in the domain of social networking sites.

A point of interest (POI) is a location that users can find something interesting. The POI information is typically made by the POI service provider and users can also add comments to a POI or add a new POI to enrich the POI contents. However, the information does not update frequently and some new points or time-sensitive events may not be marked in the POI service. Therefore, we construct a framework to extract the events from microblog to support an event-based POI service different from traditional POI service. With this kind of application, users can easily to get the events around them. In Web 2.0, microblog has become an important media spreading information in the web. With the help of mobile devices, microblogs can spread the information with greater spatiotemporal sensitivity. We take the spatial and temporal features of messages in microblogs to detect spatiotemporal events and use it to enrich POI service. This work aims at detecting spatiotemporal events in microblogs with an pair {location, time} from an handset device and feedbacks information to the users. To achieve this goal, we propose a framework with 4 phases: (1) profile construction; (2) feature extraction; (3) event summary detection; (4) ranking events. In this paper, we propose STF(standing for Spatio-Temporal Focus) value to evaluate the distinctiveness of a feature. Furthermore, we combine STF value with document overlap to expand an event cluster from an event seed. To extract the top-k possible event summaries, an efficient top-k soft clustering algorithm is proposed in this paper. In the experiments, we use real data set from Twitter to verify our proposed framework.