Secure Spatial Top-k Query Processing via Untrusted Location-Based Service Providers

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.

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.

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.

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.

Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques for spatial query processing and optimization in an in-memory and distributed setup to address scalability. More specifically, we introduce new techniques for handling query skew that commonly happens in practice, and minimizes communication costs accordingly. We propose a distributed query scheduler that uses a new cost model to minimize the cost of spatial query processing. The scheduler generates query execution plans that minimize the effect of query skew. The query scheduler utilizes new spatial indexing techniques based on bitmap filters to forward queries to the appropriate local nodes. Each local computation node is responsible for optimizing and selecting its best local query execution plan based on the indexes and the nature of the spatial queries in that node. All the proposed spatial query processing and optimization techniques are prototyped inside Spark, a distributed memory-based computation system. Our prototype system is termed LocationSpark. The experimental study is based on real datasets and demonstrates that LocationSpark can enhance distributed spatial query processing by up to an order of magnitude over existing in-memory and distributed spatial systems.

Several applications of Wireless Sensor Networks (WSN) demand information only from sub-regions of the monitored area. The user establishes the region of interest (RoI) and requires the WSN to collect data only inside this region. This kind of query is called spatial query. The state of the art of spatial query processing considers, in general, that nodes do not fail. However, nodes can be destroyed, interference can deny the communication and nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes a fault-tolerant energy efficient in-network spatial query processing mechanism for WSN. The proposed mechanism employs failure detection algorithms in order to avoid failed neighbors when forwarding and processing spatial queries. Our contributions are lightweight and resilient because they operate on demand and avoid the use of routing tables.

In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an indexing + MapReduce'' data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.

Data mining technologies are widely used to mine the interesting patterns and data from the huge collections of data. This work is intended to develop and implement an efficient, economic and customized software for trip plan for final customers. The main functions of this software are listed in the following. In this itinerary planning system, the users specify source, destination, days of travel and budget, then the system will generate a set of Points Of Interests (POIs) for a given destination, so the users can select their customized points of interests and can plan travels with k-days itinerary. All POIs are considered and ranked based on the users preference. Nearby POIs are put in the same day itinerary for the effective utilization of the plan. Inconsistent and incomplete data are removed and the list of POIs will be generated using the Clustering technique so that users can select their interested POIs. Shortest path will be displayed once they select their customized POIs. Frequent visits and suggestions for a particular destination are listed to the customers using Association rule mining technique. Clustering takes major advantage to compute the data and it has fast processing time. Each cluster holds the number of countries with the relevant information and the list of points of interests for a specified cost. With the help of shortest path, users can understand and view what is the travel distance between the POIs. Users can delete the POIs if they have relatively long distance to visit and travel. Using the association rule mining technique users can know the frequent visits of a particular destination, and it helps the system to predict the travelling patterns of the people. In this work, a customized itinerary planning service is provided, and the users can plan for multiday itineraries, designed effectively by managing the cost and time to the users.

Modern location-based applications rely extensively on the efficient processing of spatial data and queries. Spatial query engines are commonly engineered as an extension to a relational database or a cluster-computing framework. Large parts of the spatial processing runtime is spent on evaluating spatial predicates and traversing spatial indexing structures. Typical high-level implementations of these spatial structures incur significant interpretive overhead, which increases latency and lowers throughput. A promising idea to improve the performance of spatial workloads is to leverage native code generation techniques that have become popular in relational query engines. However, architecting a spatial query compiler is challenging since spatial processing has fundamentally different execution characteristics from relational workloads in terms of data dimensionality, indexing structures, and predicate evaluation. In this paper, we discuss the underlying reasons why standard query compilation techniques are not fully effective when applied to spatial workloads, and we demonstrate how a particular style of query compilation based on techniques borrowed from partial evaluation and generative programming manages to avoid most of these difficulties by extending the scope of custom code generation into the data structures layer. We extend the LB2 main-memory query compiler, a relational engine developed in this style, with spatial data types, predicates, indexing structures, and operators. We show that the spatial extension matches the performance of specialized library code and outperforms relational and map-reduce extensions.

Although a number of methods have been proposed to process exact match or range queries in P2P network, very few attention has been paid on spatial query process. In this paper, we propose a triangular topology of P2P network and spatial query processing methods on this topology. Each node maintains a set of pointers to neighbor nodes determined by delaunay triangulation. This triangular network topology provides us with an efficient way to find a path from any source node to the destination node or area, and to process spatial queries. We compare two spatial query processing methods based on the triangular network by experiments.

Local Differential Privacy (LDP) has been widely recognized as a powerful tool for providing a strong theoretical guarantee of data privacy to data contributors against an untrusted data collector. Under a typical LDP scheme, each data contributor independently randomly perturbs their data before submitting them to the data collector, which in turn infers valuable statistics about the original data from received perturbed data. Common to existing LDP mechanisms is an inherent trade-off between the level of privacy protection and data utility in the sense that strong data privacy often comes at the cost of reduced data utility. Frequency estimation based on Randomized Response (RR) is a fundamental building block of many LDP mechanisms. In this paper, we propose a novel Joint Randomized Response (JRR) mechanism based on correlated data perturbations to achieve locally differentially private frequency estimation. JRR divides data contributors into disjoint groups of two members and lets those in the same group jointly perturb their binary data to improve frequency-estimation accuracy and achieve the same level of data privacy by hiding the group membership information in contrast to the classical RR mechanism. Theoretical analysis and detailed simulation studies using both real and synthetic datasets show that JRR achieves the same level of data privacy as the classical RR mechanism while improving the frequency-estimation accuracy in the overwhelming majority of the cases by up to two orders of magnitude.

With the pervasiveness of location-aware mobile electronic devices and the advances of wireless communications, location-based services (LBS), which can help people enjoy a convenient life, has attracted considerable interest recently. However, the privacy issues of LBS are still challenging today. Aiming at the challenges, in this paper, we present a new efficient and privacy-preserving LBS query scheme in outsourced cloud, i.e., EPQ, for pervasive smartphones. In the EPQ scheme, the LBS provider's data are first outsourced to the cloud server in an encrypted manner, and then, a registered user can get accurate LBS query results without divulging his/her location information to the LBS provider and the cloud server. Specifically, based on an improved homomorphic encryption technique over a composite order group, a special spatial range query algorithm SRQC over ciphertext is proposed, with which EPQ achieves privacy preservation of user's query and confidentiality of LBS data in the outsourced cloud server. Through detailed security analysis, we show that EPQ can resist various known security threats. In addition, we also implement EPQ over a smartphone and three workstations with a real LBS data set, and extensive simulation results further demonstrate that the proposed EPQ scheme is highly efficient at the smartphone side and can be implemented effectively in the cloud server.

Recent research developments indicate that citizens’ observatories, a novel approach for data collection, management and governance, can provide valuable contributions to strategic delta planning processes. Most citizen observatories are limited in spatial coverage during data collection, according to the citizens’ availability and static locations. However, there are times in which citizens participate as trained volunteers during data collection field campaigns. In this later scenario, an important aspect in organising and maintaining citizen observatories is having a clear plan for data gathering activities, including determination of routes to be followed by these citizens. This article addresses the issue of determining such routes (called pathways) related to the specific problem of gathering data in deltaic areas, which are composed of intricate canals and wetlands. Data collection activities consist of citizens acquiring images and videos with mobile phones at predetermined locations (Points of Interest) that are only accessible by boats. A pathway selection approach is presented, supported by a hydrodynamic model, developed to capture the specific processes of the delta. The aim of the approach is to define: where to send the citizens to fulfil stakeholders’ interest and planning goals, how to reach these points and which pathways should be selected (prioritised based on such interests), considering possible flood patterns. The developed methodology is part of an ongoing H2020 funded research. The proof of concept is carried out in the Sontea-Fortuna area, of the Danube Delta, which, similar to many wetlands, contains a large variety of unique, endemic species. With the developed approach, pathways to be followed by citizens during data collection campaigns were generated and scored considering local interests. Analysis of the boat’s accessibility into the delta during different hydrological scenarios showed that the wetland is more accessible than expected, hence the proposed pathway approach was useful in prioritizing some canals over others. The determined pathways will be applied in field campaigns. The approach can be applied to other delta regions with different environmental problems, such as water quality or ecosystem assessment, contributing to the organisation of effective active citizen data collection.

The broadcasting of spatial data together with an index structure is an effective way of disseminating data in a wireless mobile environment. Mobile clients requesting data tune into a continuous broadcast only when spatial data of interest and relevance is available on the channel and thus minimize their power consumption. A mobile client experiences latency (time elapsed from requesting to receiving data) and tuning time (the amount of time spent listening to the channel). This paper studies the execution of spatial queries on broadcasted tree-based spatial index structures. The focus is on queries that require a partial traversal of the spatial index, not only a single-path root-to-leaf search. We present techniques for processing spatial queries while mobile clients are listening to a broadcast of the tree. Our algorithms can handle clients with limited memory, trees broadcast with a certain degree of replication of index nodes, and algorithms executed at the clients may employ different data structures. Experimental work on R*-trees shows that these techniques lead to different tuning times and different latencies. Our solutions also lead to efficient methods for starting the execution of a query in the middle of a broadcast cycle. Spatial query processing in a multiple channel environment is also addressed.

A partial index for distributed broadcasting in wireless mobile networks