{"title":"Popularity-based multiple-replica cloud storage integrity auditing for big data","authors":"","doi":"10.1016/j.future.2024.107534","DOIUrl":null,"url":null,"abstract":"<div><div>Multiple-replica cloud storage is employed to store multiple replicas of the user’s data in different cloud servers, which remarkably enhances the data availability. To ensure data replicas are correctly stored in cloud servers, multiple-replica cloud storage integrity auditing is proposed. Nevertheless, storing multiple replicas in cloud is not always necessary for all data, which reduces the storage efficiency for big data. In this paper, we consider a new problem of how to make a tradeoff between data availability and storage efficiency for cloud storage integrity auditing. When a file changes from an unpopular file to a popular one, this file will not be viewed as an important file any more. If we continue to store multiple replicas of the file for good availability, it will waste a lot of storage resources in big data scenario. Therefore, the tradeoff between data availability and storage efficiency is a significant issue. We propose a novel scheme called popularity-based multiple-replica cloud storage integrity auditing scheme. We introduce <span><math><mrow><mi>p</mi><mi>o</mi><mi>p</mi><mi>u</mi><mi>l</mi><mi>a</mi><mi>r</mi><mi>i</mi><mi>t</mi><mi>y</mi></mrow></math></span> into the multiple-replica cloud storage integrity auditing scheme to intelligently depict the data importance. For unpopular cloud data (important data), we adopt multiple-replica cloud storage technique to store them. In contrast, we only store a single replica for popular cloud data (unimportant data). Our proposed scheme can smoothly perform the auditing task for both unpopular cloud data and popular cloud data. As a result, it makes a nice balance between data availability and storage efficiency of cloud storage integrity auditing for big data. Furthermore, we discuss how to support the possible changes in data popularity after dynamic operations. We prove the security and make performance analysis for the proposed scheme.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24004989","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Multiple-replica cloud storage is employed to store multiple replicas of the user’s data in different cloud servers, which remarkably enhances the data availability. To ensure data replicas are correctly stored in cloud servers, multiple-replica cloud storage integrity auditing is proposed. Nevertheless, storing multiple replicas in cloud is not always necessary for all data, which reduces the storage efficiency for big data. In this paper, we consider a new problem of how to make a tradeoff between data availability and storage efficiency for cloud storage integrity auditing. When a file changes from an unpopular file to a popular one, this file will not be viewed as an important file any more. If we continue to store multiple replicas of the file for good availability, it will waste a lot of storage resources in big data scenario. Therefore, the tradeoff between data availability and storage efficiency is a significant issue. We propose a novel scheme called popularity-based multiple-replica cloud storage integrity auditing scheme. We introduce into the multiple-replica cloud storage integrity auditing scheme to intelligently depict the data importance. For unpopular cloud data (important data), we adopt multiple-replica cloud storage technique to store them. In contrast, we only store a single replica for popular cloud data (unimportant data). Our proposed scheme can smoothly perform the auditing task for both unpopular cloud data and popular cloud data. As a result, it makes a nice balance between data availability and storage efficiency of cloud storage integrity auditing for big data. Furthermore, we discuss how to support the possible changes in data popularity after dynamic operations. We prove the security and make performance analysis for the proposed scheme.
期刊介绍：
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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