Spatial Range Queries Research Articles

Toward Answering Federated Spatial Range Queries Under Local Differential Privacy

Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client’s location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients’ locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients’ reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries’ utility.

An Enhanced Partitioning Approach in SpatialHadoop for Handling Big Spatial Data

SpatialHadoop could handle spatial data operations in a low partitioning execution time compared to the traditional Hadoop. However, developing an efficient and an accurate partitioning algorithm is still a research field opened to many researchers. Confidently, this paper proposes a Minimum Boundary Rectangle-aware Priority R-Tree (MBR-aware PR-Tree) as an enhanced partitioning algorithm applicable at SpatialHadoop. Compared to state-of-art partitioning algorithms, our proposed algorithm outperforms them in terms of query execution time, file size, number of partitions, indexing time, and number of returned objects. The experimental results show superiority of our algorithm which have been confirmed for both spatial range query and k-nearest-neighbour query through evaluating the performance in different scenarios using a real dataset.

Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data

Location-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient.

Efficient Privacy-Preserving Spatial Range Query Over Outsourced Encrypted Data

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.

A neural database for differentially private spatial range queries

Mobile apps and location-based services generate large amounts of location data. Location density information from such datasets benefits research on traffic optimization, context-aware notifications and public health (e.g., disease spread). To preserve individual privacy, one must sanitize location data, which is commonly done using differential privacy (DP). Existing methods partition the data domain into bins, add noise to each bin and publish a noisy histogram of the data. However, such simplistic modelling choices fall short of accurately capturing the useful density information in spatial datasets and yield poor accuracy. We propose a machine-learning based approach for answering range count queries on location data with DP guarantees. We focus on countering the sources of error that plague existing approaches (i.e., noise and uniformity error) through learning, and we design a neural database system that models spatial data such that density features are preserved, even when DP-compliant noise is added. We also devise a framework for effective system parameter tuning on top of public data, which helps set important system parameters without expending scarce privacy budget. Extensive experimental results on real datasets with heterogeneous characteristics show that our proposed approach significantly outperforms the state of the art.

Verifiable Spatial Range Query Over Encrypted Cloud Data in VANET

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.

TrajMesa: A Distributed NoSQL-Based Trajectory Data Management System

With the development of positioning technology, a large number of trajectories have been generated, which are very useful for many urban applications. However, it is challenging to manage trajectory data for its spatio-temporal dynamics and high-volume properties. Existing trajectory data management frameworks suffer from efficiency or scalability problem, and only support limited trajectory query types. This paper takes the first attempt to build a holistic distributed NoSQL trajectory storage engine, named TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa can manage a prohibitively large number of trajectories, and support plenty of query types efficiently. Specifically, we first design a novel trajectory storage schema, which reduces the storage size tremendously. We then devise a novel indexing key schema for time ranges, based on which ID temporal query can be supported efficiently. To reduce the amount of retrieved trajectory data for a spatial range query, we innovatively propose a position code to indicate the spatial location of trajectories accurately. We also propose a bunch of pruning strategies for similarity query and k-NN query in the NoSQL environment. Extensive experiments are conducted using two real datasets and one synthetic dataset, verifying the powerful query efficiency and scalability of TrajMesa.

Enabling Efficient Spatial Keyword Queries on Encrypted Data With Strong Security Guarantees

Structured Encryption (STE), which allows a server to provide secure search services on encrypted data structures, has been widely investigated in recent years. To meet expressive search requirements in practical applications, a large number of STE constructions have been proposed either on textual keywords or spatial data. However, STE on spatio-textual data, which are widely used in location-based services, has not been fully investigated. In this paper, we formally define the notion of Spatial Keyword Structured Encryption (SKSE) and propose several concrete SKSE constructions with various efficiency-security trade-offs. Firstly, we propose a basic construction with linear search complexity, which only leaks the private files matching both spatial range query and all query keywords. Then, to improve the search efficiency on large-scale datasets, we present a novel tree-based construction with sub-linear search complexity. Finally, we introduce a post-validation approach to remove false positives and further improve storage and search performance. Our constructions are general in the sense that they can be constructed from any hidden vector encryption schemes, including public-key setting and symmetric-key setting, which can meet different sharing requirements. Our rigorous security analysis and comprehensive performance evaluation demonstrate that the proposed constructions are secure and outperform the start-of-the-art solutions.

A High-Performance Spatial Range Query-Based Data Discovery Method on Massive Remote Sensing Data via Adaptive Geographic Meshing and Coding

Many countries and organizations have been sharing a vast amount of remote sensing (RS) images across the Internet via their portals to promote the flourishing development of RS applications. These portals provide the data discovery ability to the public with necessary ways. Spatial range query is one of them to enable data discovery on RS images by their spatial extents. This article first analyzes the technical challenges in building a spatial index based on the key-value model. Then, this article presents how our proposed adaptive geographic meshing and coding method solves these challenges. The proposed method transforms the spatial extent of RS images to key-value-based indexing records to ensure spatial proximity as well as a low ratio of data duplication in indexing records. This article also shows how the proposed query selectivity strategy improves the performance of spatial range queries on indexing records. The strategy determines the order of two essential operations in a spatial range query, namely, the data deduplication and the spatial relationship computation. Finally, we implement a prototype system based on HBase and develop two MapReduce-based algorithms to speedup the process of the index construction and the spatial range query. Extensive experiments on a real dataset demonstrate that our proposed method owns a better query performance as well as a lower space overhead than competitors to handle spatial range queries on RS images.

Towards Spatial Range Queries Under Local Differential Privacy

Towards Spatial Range Queries Under Local Differential Privacy

Spatial Data Indexing and Query Processing in GeoCloud

Abstract GeoCloud is essential for spatial data management. This article depicts GeoCloud and SpatialHadoop, both of which are developed for spatial information, indexing, and query processing. It contains traditional spatial indexing that comprises R-tree, Hilbert R-tree, and improved Bloom filter tree. We enhance the query search by utilizing Spatial Join, Range Query, k-nearest neighbor (k-NN), and Max k-NN queries. By doing so, we implement the data structures and query evaluation performance of different spatial datasets in GeoCloud instances with SpatialHadoop. We show that our proposed system is more efficient in terms of data storage and retrieval in GeoCloud.

Facilitating Secure and Efficient Spatial Query Processing on the Cloud

Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality of the data or suffer from high communication cost between the server and the user. To overcome this problem, we propose a dual transformation and encryption scheme for spatial data, where encrypted queries are executed entirely at the service provider on the encrypted database and encrypted results are returned to the user. The user issues encrypted spatial range queries to the service provider and then uses the encryption key to decrypt the query response returned. This allows a balance between the security of data and efficient query response as the queries are processed on encrypted data at the cloud server. Moreover, we compare with existing approaches on large datasets and show that this approach reduces the average query communication cost between the authorized user and service provider, as only a single round of communication is required by the proposed approach.

Enhancing Privacy Preserving Query Retrieval Using Locality Sensitive Hashing

The usage of smart phones is tremendously increasing day by day. Due to this, Location Based Services (LBS) attracted considerably and becomes more popular and vital in the area of mobile applications. On the other hand, the usage of LBS leads to potential threat to user’s location privacy. In this paper, the famous LBS provide information about points of interest (POI) in spatial range query within a given distance. For that, a more efficient and an enhanced privacy-preserving query solution for location based, Efficient Privacy-Location Query (EPLQ) is proposed along with Locality Sensitive Hashing (LSH) reduces the dimensionality of high dimensional data. Experiments are conducted extensively and the results show the efficiency of the proposed algorithm EPLQ in privacy preserving over outsourced encrypted data in spatial range queries. The proposed method performs in spatial range queries and similarity queries of privacy preserving.

A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash

Geographic meshing system is an essential technology in the digital earth framework and has essential applications in the integration and organization of heterogeneous spatial data, along with corresponding coding method. But current meshing and coding methods show unsatisfying locality and performance. To make an improvement, an adaptive Hilbert-Geohash meshing and coding method called AHG is proposed, which could represent both the location and the approximate size of the coded object directly by the meshing hierarchy and the corresponding coding length. This unique feature helps to accelerate the spatial range query and neighbor query. By simple string operations, many candidate objects that do not meet the query criteria can be quickly filtered out without precise spatial calculation. In addition, AHG code can also support spatial size query by finding objects whose size is within a certain interval quickly, without calculating the precise size of each object, which brings great convenience to spatial size statistics of a massive spatial dataset. Demonstrated by experiments over different types of the spatial dataset in a common PC environment, AHG shows favorable stability and scalability besides its capability in accelerating spatial query. The method is now applied successfully in several spatial query tools in a high-performance geographic information system called HiGIS.

A novel method for parallel indexing of real time geospatial big data generated by IoT devices

IoT produces a huge amount of big data as it comprises billions of devices that are interconnected with each other through internet. Today’s majority of the big data part is about geospatial data and every year it increases rapidly. In order to process such massive real time geospatial big data, we must have scalable, efficient indexing method. R Tree and its variants have emerged as most efficient, widely accepted and have adopted indexing method for the management and processing of geospatial data. Current literature on parallel construction of R Tree indexes of geospatial data has disadvantages, that all the methods considered only two dimensional geospatial data and all are based on MapReduce framework. As the number of dimension increases, complexity of index creation is also increases along with this MapReduce framework has lots of disadvantages such as, it works only on static data, consumes a lot of disk space and time, which leads to high latency and fault tolerance of the entire system. In order to overcome these issues, a novel method for parallel construction of R Tree and its variants, use of the Apache Spark (in-memory and on-disk computation) based on the IoT Zetta platform is proposed. The main purpose of using Apache Spark is to index real time geospatial data for continuously updating the position of aircraft in real time while indexing it in R tree and its variants, so that spatial range query can fetch real time results and Apache Spark is much faster as compared to MapReduce framework. The extensive experimental results show that our parallel generated R tree and its variants retains similar properties as of sequential generated R tree and its variants with the excellent scalability and reducing a significant amount of time for the construction of index, index updating & executing spatial range query over geospatial data by exploiting the latest parallelism framework.

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.

LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data

Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems.

Computing over encrypted spatial data generated by IoT

Proliferation of IoT devices produces the enormous amount of data that need to be stored on clouds. A main focus of this paper is to ensure the secrecy of data, while it is in transit through unsecure communication media from edge devices to cloud and to provide security to the data once it is stored on public cloud. A new techniques based on computing over encrypted data i.e. homomorphic encryption seems to be promising methods. However, most of the previous works supporting computing over encrypted data are neither efficient nor compatible with IoT data and real time spatial data streams because there is a huge difference between normal data and spatial data. In this paper, we proposed a framework for computing over IoT generated encrypted spatial data. In order to provide computation over encrypted data first it needs to be indexed in standard data structure. For indexing encrypted data, we used R tree and its variants. We also proposed a method of most efficient and scalable, parallel construction of R trees and its variants on real time encrypted spatial data. We fired spatial range queries on encrypted spatial data. Specifically, the spatial range query execution time over encrypted spatial data of our proposed scheme is extremely efficient which takes slightly more time as taken by normal spatial range query executed over non-encrypted real time spatial data. Our scheme is not only efficient, but also highly compatible and scalable with IoT generated spatial data. Moreover, we rigorously define the scalability, query performance time, analyze the security of our schemes, and also conduct extensive experiments with a real time spatial dataset to demonstrate the performance of our schemes.

SRC: geospatial visual analytics belongs to database systems

GeoVisual analytics, abbr. GeoViz , is the science of analytical reasoning assisted by GeoVisual map interfaces. For example, a GeoViz heat map of the New York City taxi trips tells the taxi company where the hot pick-up and drop-off locations are. GeoViz involves the following two memory and compute intensive phases: Phase I: Spatial Data Preparation: In this phase, the system first loads the designated spatial data from the database (e.g., Shape files, PostGIS, HDFS). Based on the application, the system may then need to perform data processing operations (e.g., spatial range query, spatial join query) on the loaded spatial data to return the set of spatial objects to be visualized. Phase II: Map Visualization: In this phase, the system applies the map visualization effect, e.g., Heatmap, on the spatial objects produced in the Phase I. The system first pixelizes the spatial objects, then aggregates overlapped pixels, and finally renders an image for each geospatial map tile.

A NOVEL APPROACH OF INDEXING AND RETRIEVING SPATIAL POLYGONS FOR EFFICIENT SPATIAL REGION QUERIES

Abstract. Spatial region queries are more and more widely used in web-based applications. Mechanisms to provide efficient query processing over geospatial data are essential. However, due to the massive geospatial data volume, heavy geometric computation, and high access concurrency, it is difficult to get response in real time. Spatial indexes are usually used in this situation. In this paper, based on k-d tree, we introduce a distributed KD-Tree (DKD-Tree) suitbable for polygon data, and a two-step query algorithm. The spatial index construction is recursive and iterative, and the query is an in memory process. Both the index and query methods can be processed in parallel, and are implemented based on HDFS, Spark and Redis. Experiments on a large volume of Remote Sensing images metadata have been carried out, and the advantages of our method are investigated by comparing with spatial region queries executed on PostgreSQL and PostGIS. Results show that our approach not only greatly improves the efficiency of spatial region query, but also has good scalability, Moreover, the two-step spatial range query algorithm can also save cluster resources to support a large number of concurrent queries. Therefore, this method is very useful when building large geographic information systems.