Multi-dimensional Range Queries Research Articles

Random Data Distribution for Efficient Parallel Point Cloud Processing

Abstract. Current point cloud data management systems and formats are heavily specialized and targeted solely towards visualization purposes and fail to address the diverse needs of progressive point cloud workflows like for example semantic segmentation using machine learning. We therefore propose a distributed data infrastructure for dynamic point cloud data management that can support interactive real-time visualization at scale while simultaneously serving as a platform for analytical tasks. By introducing random data distribution, we show that simple query fragmentation and efficient and effective parallelism at scale are possible. At the same time, arbitrary queries in space and time can be efficiently run over the infrastructure including query semantics which returns only a random sample of the query results or preferred points based on an importance dimension calculated, for example, from a local point density information as commonly done in point cloud visualization. To cope with the unknown amount of user-specific attributes and to support even multiple ways of deciding the importance of a given point (ground point removal, coverage of space, random subset) the system is designed to support all of them transparently as multidimensional range queries backed by spatial indices.

Fast and private multi-dimensional range search over encrypted data

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

Adaptive Indexing of Objects with Spatial Extent

Can we quickly explore large multidimensional data in main memory? Adaptive indexing responds to this need by building an index incrementally, in response to queries; in its default form, it indexes a single attribute or, in the presence of several attributes, one attribute per index level. Unfortunately, this approach falters when indexing spatial data objects, encountered in data exploration tasks involving multidimensional range queries. In this paper, we introduce the Adaptive Incremental R-tree (AIR-tree): the first method for the adaptive indexing of non-point spatial objects; the AIR-tree incrementally and progressively constructs an in-memory spatial index over a static array, in response to incoming queries, using a suite of heuristics for creating and splitting nodes. Our thorough experimental study on synthetic and real data and workloads shows that the AIR-tree consistently outperforms prior adaptive indexing methods focusing on multidimensional points and a pre-built static R-tree in cumulative time over at least the first thousand queries.

PMRQ: Achieving Efficient and Privacy-Preserving Multidimensional Range Query in eHealthcare

Healthcare data explosion and cloud computing booming have motivated healthcare centers to outsource their healthcare data and data-driven services to a powerful cloud. Nevertheless, due to privacy concerns, the data are usually encrypted before being outsourced, which will degrade the data utility and make it challenging to implement data-driven services. Although the multidimensional range query over encrypted data, as one of the most popular outsourced services in eHealthcare, has been extensively studied, existing solutions still have some limitations in efficiency, privacy, and practicality. Aiming at this challenge, in this article, we design an efficient and privacy-preserving multidimensional range query (PMRQ) scheme. We first build an R-tree to index the data set and reduce the R-tree-based range queries to the multidimensional range intersection problem. Then, by delicately designing a data comparison algorithm and a homomorphic encoding technique, we present an encoding-based range intersection algorithm. After that, by employing matrix encryption to protect the privacy of the encoding-based range intersection algorithm, we design a multidimensional range intersection predicate encryption (MRIPE) scheme. Based on the MRIPE scheme, we then propose our PMRQ scheme. A detailed security analysis illustrates that our PMRQ scheme is privacy preserving, and experimental results demonstrate that it is computationally efficient.

Towards Practical and Privacy-Preserving Multi-Dimensional Range Query Over Cloud

It is undeniable that Internet of Things (IoT) in big data era can provide us with huge volumes of multi-dimensional data, transforming our society into a much more intelligent one. In order to fit for the multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received considerable attention in recent years. However, as the cloud server is not fully trustable, designing multi-dimensional range queries over encrypted data becomes a research trend, and many solutions have been proposed in the literature. Nevertheless, most existing solutions suffer from the leakage of the single-dimensional privacy, and such leakage would severely put the data at risk. Although a few existing works have addressed the problem of single-dimensional privacy, they are impractical in some real scenarios due to the issues of inefficiency, inaccuracy, and two-cloud-server requirement. Aiming at solving these issues, in this article, we propose a practical and privacy-preserving multi-dimensional range query (PRQ) scheme. Specifically, in our proposed PRQ scheme, we first index the multi-dimensional dataset with an R-tree and reduce R-tree based range queries to the problem of point intersection and range intersection. Then, by employing the lightweight matrix encryption technique, we design two novel algorithms for PRQ, i.e., multi-dimensional point intersection predicate encryption (PIPE) and multi-dimensional range intersection predicate encryption (RIPE), which can preserve the privacy of the proposed point intersection algorithm and range intersection algorithm, and further preserve the single-dimensional privacy of the proposed PRQ scheme. Detailed security analysis shows that our proposed PRQ scheme is indeed privacy-preserving. In addition, extensive simulations are conducted, and the results also demonstrate its efficiency.

A Privacy-Preserving Multidimensional Range Query Scheme for Edge-Supported Industrial IoT

Edge-supported Industrial Internet of Things (IIoT) has recently received significant attention since the edge computing can greatly improve the service quality of IIoT applications. However, edge servers are not fully trusted and are often deployed at the edge of the network. Therefore, there are some security challenges that need to be addressed. For edge-supported IIoT, a privacy-preserving range query is one of the most important functional requirements. Recently, some privacy-preserving range query solutions have been proposed in different fields. However, most of them only support single-dimensional range query, which are inefficient for the requirement of multidimensional range query. To address these problems, we propose a privacy-preserving multidimensional range query scheme for edge-supported IIoT, called Edge-PPMRQ, in this article. In Edge-PPMRQ, a novel range division algorithm is designed, through which the multidimensional ranges can be merged into one range, so as to achieve multidimensional range query through one query request. In addition, Edge-PPMRQ also supports the range queries for continuous, discontinuous, and arbitrary boundary ranges. The detailed security analysis proves that Edge-PPMRQ is privacy preserving for the query ranges, the query results, and the sensed data of IIoT devices. Furthermore, extensive comparison experiments also illustrate that Edge-PPMRQ is efficient in communication and computation.

Secure and efficient multi-dimensional range query algorithm over TMWSNs

With the development of the 5G and IoT technology, a new mobile edge computing network, TMWSN (Tiered Mobile Wireless Sensor network), is widely used in military and civilian fields due to their extensibility, energy efficiency and flexibility. However, as the crucial node of the TMWSNs, the storage node is vulnerable to be attacked, which undermines the privacy and integrity of the query results.In this paper, we mainly study the multi-dimensional range query algorithm over TMWSNs. First, we propose a baseline algorithm based on MR-Tree. It uses MR-Tree to index multi-dimensional sensory data, and facilitate the efficient processing of multi-dimensional range query and the establishment of validation set. However, the baseline algorithm returns a large amount of validation data, resulting in plenty of communication overhead. To solve this problem, we further propose an algorithm based on multi-dimensional bucket, which takes a bucket as a transmission unit, uses the range of sensory data in a bucket as an index, and verifies the integrity of the query results by checking the correlation of sensory data in a bucket. Theoretical and experimental results show that, in most cases, the algorithm based on the multi-dimensional bucket can effectively reduce communication overhead while ensuring privacy and integrity.

MDOPE: Efficient multi-dimensional data order preserving encryption scheme

Nowadays, tremendous information technology industries resort to cloud servers to store data with an outsourcing approach to extend their storage and computation power. This, however, also leads to privacy and security issues of unprotected data against curious cloud servers. The most common solution currently is to encrypt the data before uploading it. The problem is that encrypted data is out of control and users also cannot transfer the searching job to cloud serves anymore. Specifically, the order preserving encryption (OPE) provides an efficient solution to the order of plaintexts. Existing OPE schemes focus on single-dimensional data, and fail to effectively process multi-dimensional data. In this paper, we propose a multi-dimensional data order preserving encryption scheme MDOPE allowing fine-grained multi-dimensional range queries. Our scheme constructs query indexes for each dimension of the data based on the order preserving encryption network. In particular, the proposed scheme ensures that no external entity, including the cloud server, can obtain additional information other than the order of ciphertexts during the whole query process.

A Security Multi-Dimensional Range Query Protocol Based on Left 0-1 Encoding in Two-Tiered Wireless Sensor Networks

In this research on range query technology for two-tiered wireless sensor networks, the existing researches still need improvement in privacy security and communication cost of the sensor node. In this paper, a security multi-dimensional range query protocol is proposed. The left 0-1 encoding mechanism (a new and optimized encoding mechanism), HMAC algorithm, and BASE64 encoding are employed to optimize the calculation of comparison factor. The encryption index chain is constructed by using AES algorithm to replace the long encryption constraint chain, so as to lower the energy consumption of computation and communication. The base station can verify the authenticity and integrity of query results on the basis of continuity of storage time and encryption index chain. The experiment has been conducted in the real systems. The protocol is compared and analyzed with the latest proposed Communication-Efficient Secure Range Queries (CSRQ). The results have shown that the proposed protocol can gain a better performance in terms of four aspects, i.e., the length of sensing data, the number of data collected in each cycle, the number of data dimensions and the number of sensor nodes in each unit. Meanwhile, compared with CSRQ, the communication cost of the proposed protocol is reduced by at least 10%.

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.

An efficient indexing structure for multi-dimensional range query

An efficient indexing structure for multi-dimensional range query

Answering multi-dimensional range queries under local differential privacy

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlations among attributes, avoiding the curse of dimensionality, and dealing with the large domains of attributes. None of the existing approaches satisfactorily deals with all three challenges. Overcoming these three challenges, we first propose an approach called Two-Dimensional Grids (TDG). Its main idea is to carefully use binning to partition the two-dimensional (2-D) domains of all attribute pairs into 2-D grids that can answer all 2-D range queries and then estimate the answer of a higher dimensional range query from the answers of the associated 2-D range queries. However, in order to reduce errors due to noises, coarse granularities are needed for each attribute in 2-D grids, losing fine-grained distribution information for individual attributes. To correct this deficiency, we further propose Hybrid-Dimensional Grids (HDG), which also introduces 1-D grids to capture finer-grained information on distribution of each individual attribute and combines information from 1-D and 2-D grids to answer range queries. To make HDG consistently effective, we provide a guideline for properly choosing granularities of grids based on an analysis of how different sources of errors are impacted by these choices. Extensive experiments conducted on real and synthetic datasets show that HDG can give a significant improvement over the existing approaches.

Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED $^+$ . By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED $^+$ surpass the state-of-the-art methods in privacy-preservation level and efficiency.

Efficient and secure multi-dimensional geometric range query over encrypted data in cloud

Secure geometric range query, which aims to retrieve data points within a given geometric range from an encrypted dataset in the cloud, attracts more and more attention due to its wide applications. Up to now, several secure geometric range query schemes have been put forward. However, the existing schemes still suffer from various disadvantages, such as they are of low efficiency, cannot support multi-dimensional data and general range query, or even have security flaws. In this paper, we study secure geometric range query on encrypted dataset in cloud. First, we show the security problem of the state-of-the-art scheme by proposing an efficient attack method. Then, we propose a new secure solution for general multi-dimensional range query, which is secure under known-background model, and leverage R-tree index to achieve sub-linear search efficiency. Finally, through theoretical analysis and extensive experiments, we demonstrate the effectiveness and efficiency of our proposed approaches.

Executing multi-dimensional range query efficiently and flexibly over outsourced ciphertexts in the cloud

Encryption is one of the most straightforward methods for ensuring the confidentiality of outsourced data on the cloud. However, encryption makes queries more difficult to perform. In recent years, new encryption schemes for facilitating queries have been proposed. However, for these schemes, some cannot support the scenario of multiple users, some are inefficient, and some are not sufficiently flexible (users must always ask the data owner for the tokens that are used for searching ciphertexts on the cloud). In this paper, we propose a scheme that supports efficient and flexible range search over ciphertexts in the scenario of multiple users. In our scheme, we construct an Encrypted Interval Tree (EIT) as the index for ciphertexts. The data owner outsources the EIT and ciphertexts to the cloud, and later distributes secret parameters (search keys, navigation paths and signatures) to users. By utilizing these secret parameters, users can generate tokens for the queried ranges without communication with the data owner and subsequently use the tokens to perform range search over ciphertexts on the cloud. Moreover, the signature technique is adopted in our scheme. Thus, the cloud can authenticate the identifiers of users and determine the legality of tokens. In this paper, we implement our scheme and conduct extensive experiments. The experimental results demonstrate the efficiency of our scheme. Finally, we analyze the security of our scheme.

Co-processing heterogeneous parallel index for multi-dimensional datasets

We present a novel multi-dimensional range query co-processing scheme for the CPU and GPU. It has been reported that traversing hierarchical tree structures in parallel is inherently not efficient because of large branching factors. Besides, it is known that the recursive tree traversal algorithm required for multi-dimensional range queries is not well suited for the GPU architecture owing to its small shared memory.In this paper, we propose co-processing range queries using both the CPU and GPU to make the most use of each architecture. In Hybrid tree that we present in this paper, we let CPU navigate the internal nodes of hierarchical tree structures and make GPU scan leaf nodes in a linear fashion using a massively large number of processing units. With the co-processing scheme, we can asynchronously leverage the strengths of each architecture. We also propose a novel dynamic GPU block scheduling algorithm for multiple range queries. In our scheduling algorithm, we consider the selection ratio of each query to determine the number of GPU blocks to launch. By assigning the right number of GPU blocks, we can significantly improve the query processing throughput for multiple concurrent queries. Our extensive experimental study shows that the proposed co-processing scheme shows up to 12× faster query response time than the state-of-the-art GPU tree traversal algorithm. We also show that our dynamic GPU block assignment algorithm improves the query processing throughput by up to 4× .

Multi-dimensional Range Query on Outsourced Database with Strong Privacy Guarantee

Multi-dimensional Range Query on Outsourced Database with Strong Privacy Guarantee

ZNS - Efficient query processing with ZurichNoSQL

NoSQL data stores have recently gained popularity as an alternative to relational database management systems since they typically do not require a fixed schema and scale well for large data sets. These systems have often been tuned to a number of very specific operations such as writing or reading of large data sets. However, none of these novel systems has been demonstrated to efficiently perform multi-dimensional range queries incorporating many boolean operators, a task which is commonly used in scientific data exploration, data warehousing and business analytics.In this paper we introduce ZurichNoSQL (ZNS) - a novel NoSQL main memory store that supports efficient processing of multi-dimensional point queries and range queries. The key idea of ZNS is to store the data in a column format (compressed column storage) similar to systems used in high performance computing. Moreover, the ZNS architecture is based on a set of low-level main memory techniques ensuring that CPU caches are being used efficiently. Our experimental results comparing to popular NoSQL stores such as FastBit, MongoDB and Spark SQL demonstrate that ZNS significantly outperforms these systems in most cases.

A PR-quadtree based multi-dimensional indexing for complex query in a cloud system

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.

Fractal: An advanced multidimensional range query lookup protocol on nested rings for distributed systems

One of the key issues in large-scale distributed systems such as P2P and grids is the capability of efficient multidimensional range query processing. Although several methods have recently been proposed for solving this problem on distributed systems, these methods have not been able to meet the fundamental necessity of a typical method on large-scale distributed systems, i.e., scalability.This paper presents Fractal, a fully decentralized and highly scalable multidimensional range query lookup protocol for distributed systems. In this work, to organize the available nodes in the system, an n-dimensional space called Key Space is utilized. The available nodes on the nested Fractal rings maintain information about only O(logN) other nodes, and the Fractal lookup protocol discovers the destination node through these nested rings with a logarithmic cost. Because of its flexibility, Fractal allows the system to create a concept called Layering, which minimizes the probability of the wide-area message transfers (WAMTs) during the lookup process. Using several criteria, Fractal is compared with several successful methods that have recently been presented. Simulation results show the efficiency and performance of Fractal in networks of different sizes.