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Cloak: Hiding Retrieval Information in Blockchain Systems via distributed query Requests

IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 3213-3226页

作者： Xiao, Jiang Chang, Jian Lin, Licheng Li, Binhong Dai, Xiaohai Xiong, Zehui Choo, Kim-Kwang Raymond Gai, Keke Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp L Wuhan 430074 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

The privacy-preserving query is critical for modern blockchain systems, especially when supporting many crucial applications such as finance and healthcare. Recent advances in blockchain query schemes mainly focus on enhancing the traceability efficiency of integrity authentication. Despite these efforts, we argue that the exposure of retrieval information may result in privacy leakage, which inevitably poses an important yet unresolved challenge. In this paper, we introduce Cloak, a novel privacy-preserving blockchain query scheme with two notable features. First, it utilizes a two-phase distributed query requests technique, i.e., division and aggregation, to hide retrieval information based on the natural independent characteristic of blockchain. Second, we add noise to the sub-request set to avoid malicious attacks during transmission and adopt smart contract-based asymmetric encryption to guarantee the correctness of query results. Experimental results demonstrate that Cloak improves the query performance by up to 4x and reduces the storage overhead by 50% compared with the state-of-the-art Spiral.

关键词： Blockchains Privacy Data privacy query processing Protection Encryption Noise Blockchain privacy-preserving distributed query lightweight client

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Hierarchical Rule Compliance Check Method for distributed query

Hierarchical Rule Compliance Check Method for Distributed Qu...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Singh, Divya Takata, Mika Kashiyama, Toshihiko Katsunuma, Satoshi Nishikawa, Norifumi Digital Platform Innovation Center Hitachi Ltd. Tokyo Japan

ISBN: (纸本)9798350324457

To improve business efficiency, it has become important to utilize data from multiple databases adhering to complex regulations. For efficient data utilization across multiple databases, the data analyst needs to prepare distributed queries in compliance with the complex regulations imposed by countries and organizations respectively. Before the prepared distributed query can be executed, it needs to go through compliance check for country and organizational regulations. Existing approaches for compliance check are inefficient as they are applicable for databases considering only one type of regulation. In this paper, we propose an efficient hierarchical compliance check method which is applicable for multiple databases adhering to regulation by country and organization. To improve efficiency a combination of country regulation checker and organization regulation checker is developed. The method first checks regulations imposed by country and then checks regulations imposed by each organization resulting in hierarchical combination. We show that our proposed compliance check method takes approximately 36.7% less time to obtain compliance status. Obtaining compliance status quickly results in increasing the interactivity of the data analyst for data utilization from multiple databases. © 2023 IEEE.

关键词： Country regulations Data Governance distributed query Organization policy Personal data and Privacy Protection

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An Improved distributed query for Large-Scale RDF Data

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Journal on Big Data 2020年第4期2卷 157-166页

作者： Aoran Li Xinmeng Wang Xueliang Wang Bohan Li College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China Key Laboratory of Safety-Critical Software Ministry of Industry and Information TechnologyNanjing211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Suzhou215000China School of Data Science and Technology Heilongjiang UniversityHarbin150080China

The rigid structure of the traditional relational database leads to data redundancy,which seriously affects the efficiency of the data query and cannot effectively manage massive *** solve this problem,we use distributed storage and parallel computing technology to query RDF *** order to achieve efficient storage and retrieval of large-scale RDF data,we combine the respective advantage of the storage model of the relational database and the distributed *** overcome the disadvantages of storing and querying RDF data,we design and implement a breadth-first path search algorithm based on the keyword query on a distributed *** conduct the LUBM query statements respectively with the selected data *** experiments,we compare query response time in different conditions to evaluate the feasibility and correctness of our *** results show that the proposed scheme can reduce the storage cost and improve query efficiency.

关键词： RDF distributed query HBase query optimization

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Analysis of distributed query Database Optimization Technology

Analysis of Distributed Query Database Optimization Technolo...

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2017 4th ICMIBI International Conference on Training, Education, and Management (ICMIBI-TEM 2017)

作者： Min Li Henan University of Finance and Economics

In this paper, we present the technology of optimizing the distributed query database. How to migrate the tasks on the overloaded computer to the light load computer is the key problem of the dynamic load balancing of the distributed system. If there is a heavy machine of this class, the heavy machine task using mobile Agent migration to the light load machine. If there is no heavy machine in the category, is request to the other server, start the dynamic load balancing algorithm. If there are other types of heavy machine, heavy machine will use mobile task Agent moved to the light load machine. Under this basis, this paper integrates the machine learning algorithms to propose the novel idea of constructing the query model that will be meaningful.

关键词： distributed query Database optimization Analysis

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distributed Knowledge Graph query Acceleration Algorithm 1

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7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) International Joint Conference on Web and Big Data

作者： Shi, Peifan Li, Youhuan Li, Wenjie Chen, Xinhuan Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Peking Univ Chongqing Res Inst Big Data Chongqing Peoples R China Tencent Inc Shenzhen Peoples R China

ISBN: (数字)9789819723874

ISBN: (纸本)9789819723867;9789819723874

As the era of big data continues to evolve, the scale of knowledge data that needs to be processed in reality is enormous, and the single-machine model is incapable of handling queries on large-scale knowledge graph data. Therefore, distributed clusters are necessary to improve processing capability. The core of the existing approaches is all by splitting the large-scale graph data into multiple copies, distributing each copy to different machines for processing, and finally merging the results. However, these approaches suffer from two problems: (i) the result of knowledge graph merging is huge, far exceeding the final result itself, resulting in a lot of data transfer overhead during the distributed merging phase;(ii) the parallelism of algorithms is limited to the physical level of machine parallelism in task partitioning and lacks computational logic parallelism, such as the merging phase, which does not achieve good parallelism. To address these issues, we propose a distributed framework for offline index construction and online SPARQL query processing framework to achieve parallel accelerated processing. Our approach can more efficiently filter candidate solutions that do not match the result, reducing the size of the results to be merged and leading to a reduction in computational and communication costs. Additionally, we also introduce additional parallelism in the mutual merging phase to improve computational efficiency and system throughput.

关键词： Knowledge Graph distributed query query Acceleration

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A Real-Time distributed query Solution Based on the OPC

A Real-Time Distributed Query Solution Based on the OPC

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Yang, Zhi Ju, Kezhen Zhang, Chunping Hu, Mu Lin, Feng State Grid Elect Power Sci Res Inst Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479966219

The distributed query is one of the research focus in the Big Data. Nowadays, many companies and institutions provide technology and products to realize function or improve efficiency in the all kinds of database. In the scene of electricity, using these techniques, the real-time requirement (<10s) cannot be met. This paper provides a real-time distributed query solution with Objectification Parallel Computing (OPC) to solve the above challenges. The data split from Big Data, is distributed stored in memory of cluster in the OPC. In the solution, making use of the thought of divide and rule and tree merging, there are two stages. The first stage is local data query. The intermediate query result can be obtained. The second stage is multistage summarizing. The final result can be returned to user. The solution has been applied to the power production management system (PMS) of State Grid of China. The results show that solution is efficiently reliable and meets real-time.

关键词： component: Big Data distributed query Objectification Parallel Computing Binary Location

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MSTDB: A Hybrid Storage-Empowered Scalable Semantic Blockchain Database

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第8期35卷 8228-8244页

作者： Zhou, Enyuan Hong, Zicong Xiao, Yang Zhao, Dongxiao Pei, Qingqi Guo, Song Akerkar, Rajendra Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Xidian Univ Sch Cyber Engn State Key Lab ISN Xian 710071 Shaanxi Peoples R China Xidian Univ State Key Lab ISN Shaanxi Key Lab Blockchain & Secure Comp Xian 710071 Shaanxi Peoples R China Western Norway Res Inst Big Data Res Grp N-6856 Sogndal Norway

Blockchain has been regarded as a trusted carrier for distributed data storage. With large volumes of valuable data stored on blockchain, data query has become a major requirement. However, the existing blockchains do not provide efficient query functionality because of their deep-rooted chain structure. Blockchain database is a new direction that constructs index on top of blockchain to provide rich query functionalities. The existing works are either insecure because the query process separates from the blockchain consensus, or inscalable because all the data needs to be stored in the block. In this paper, we propose a novel semantic blockchain database called MSTDB. We design a hybrid on/off chain blockchain storage architecture in which the majority of blockchain storage is offloaded to the off-chain storage and a novel index structure named Merkle Semantic Trie (MST) is designed to be a secure and semantic bridge between on- and off-chain. Based on MST, MSTDB provides a variety of semantic query functions including multi-keyword query, range query, Top-K query, and cross-chain query. To improve the performance further, we design some index compression and query preprocessing techniques for MSTDB. Extensive experiments demonstrate the effectiveness and efficiency of our blockchain database.

关键词： Blockchain database data sharing distributed query index

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Join queries optimization in the distributed databases using a hybrid multi-objective algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第3期25卷 2021-2036页

作者： Azhir, Elham Navimipour, Nima Jafari Hosseinzadeh, Mehdi Sharifi, Arash Unal, Mehmet Darwesh, Aso Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Gachon Univ Pattern Recognit & Machine Learning Lab 1342 Seongnamdaero Seongnam 13120 South Korea Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Nisantasi Univ Dept Comp Engn TR-34485 Istanbul Turkey Univ Human Dev Dept Informat Technol Sulaymaniyah Iraq

In the distributed database systems, the relations needed by a query can be kept in several locations. This process significantly increases potential corresponding query Execution Plans (QEP's) for a user query. Henceforth, in addition to the expense of local computing, the charge of transferring data between different cloud sites should also be considered. It does not sound logical to investigate all potential query plans in a high setting like this. The best query plan (regarding cost) must be generated for processing a given query. A new hybrid multi-objective genetic and bat algorithm, a Multi-Objective Genetic Algorithm with BAT (MOGABAT), is used in the present article to produce the best query plans. The functionality comparison is made on different join graph structures, among MOGABAT, Multi-Objective BAT (MOBAT), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results have shown that the quality of generated query plans is enhanced for the join graph structures. Nevertheless, more execution time is needed.

关键词： distributed query Multi-Objective Optimization MOBAT Genetic Algorithm

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querying distributed Sensor Streams in the Edge-to-Cloud Continuum 6

Querying Distributed Sensor Streams in the Edge-to-Cloud Con...

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6th IEEE International Conference on Edge Computing and Communications (IEEE EDGE)

作者： Karlstetter, Roman Widhopf-Fenk, Robert Schulz, Martin IfTA GmbH Puchheim Germany Tech Univ Munich Garching Germany

ISBN: (数字)9781665481403

ISBN: (纸本)9781665481403

Sensor data is of crucial importance in many IoT scenarios. It is used for online monitoring as well as long term data analytics, enabling countless use cases from damage prevention to predictive maintenance. Multivariate sensor time series data is acquired and initially stored close to the sensor, at the edge. It is also beneficial to summarize this data in windowed aggregations at different resolutions. A subset of the resulting aggregation hierarchy is typically sent to a cloud infrastructure, often via intermittent or low bandwidth connections. Consequently, different views on the data exist on different nodes in the edge-to-cloud continuum. However, when querying this data, users are interested in a fast response and a complete, unified view on the data, regardless of which part in the infrastructure continuum they send the query to and where the data is physically stored. In this paper, we present a loosely coupled approach that enables fast range queries on a distributed and hierarchical sensor database. Our system only assumes the possibility of fast local range queries on a hierarchical sensor database. It does not require any shared state between nodes and thus degrades gracefully in case certain parts of the hierarchy are unreachable. We show that our system is suitable for driving interactive data exploration sessions on terabytes of data while unifying the different views on the data. Thus, our system can improve the data analysis experience in many geo-distributed scenarios.

关键词： edge cloud multivariate sensor data stream distributed query

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A secure architecture for exploring patient-level databases from distributed institutions 35

A secure architecture for exploring patient-level databases ...

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35th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

作者： Almeida, Joao Rafael Barraca, Joao Paulo Oliveira, Jose Luis Univ Aveiro DETI IEETA Aveiro Portugal Univ Aveiro DETI IT Aveiro Portugal Univ A Coruna Dept Informat & Commun Technol La Coruna Spain

ISBN: (数字)9781665467704

ISBN: (纸本)9781665467704

One of the main goals of clinical studies consists of identifying diseases' causes and improving the efficacy of medical treatments. Sometimes, the reduced number of participants is a limiting factor for these studies, leading researchers to organise multi-centre studies. However, sharing health data raises certain concerns regarding patients' privacy, namely related to the robustness of anonymisation procedures. Although these techniques remove personal identifiers from registries, some studies have shown that anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this paper, we propose a secure architecture to explore distributed databases without compromising the patient's privacy. The proposed architecture is based on interoperable repositories supported by a common data model.

关键词： distributed query Patient privacy Data anonymisation OMOP CDM OHDSI

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