ImDMI: Improved Distributed M-Invariance model to achieve privacy continuous big data publishing using Apache Spark

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2025-03-07 DOI:10.1016/j.bdr.2025.100519
Salheddine Kabou , Laid Gasmi , Abdelbaset Kabou , Sidi Mohammed Benslimane
{"title":"ImDMI: Improved Distributed M-Invariance model to achieve privacy continuous big data publishing using Apache Spark","authors":"Salheddine Kabou ,&nbsp;Laid Gasmi ,&nbsp;Abdelbaset Kabou ,&nbsp;Sidi Mohammed Benslimane","doi":"10.1016/j.bdr.2025.100519","DOIUrl":null,"url":null,"abstract":"<div><div>One of the critical challenges in the big data analytics is the individual's privacy issues. Data anonymization models including k-anonymity and l-diversity are used to guarantee the tradeoff between privacy and data utility while publishing the data. However, these models focus only on the single release of datasets and produce a certain level of privacy. In practical big data applications, data publishing is more complicated where the data is published continuously as new data is collected, and the privacy should be achieved for different releases. In this research, we propose a new distributed bottom up approach on Apache Spark for achievement of the m-invariance privacy model in the continuous big data context. The proposed approach, which is the first study that deals with dynamic big data publishing, is based on the insertion and the split process. In the first process, the data records collected from different workers are inserted into an improved bottom up R-tree generalization in order to minimizing the information loss. The second process concentrates on splitting the overflowed node with respect to the m-invariance model requirement by minimizing the overlap between the resulting partitions. The experimental results show significant improvement in term of data utility, execution time and counterfeit data records as compared to existing techniques in the literature.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100519"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579625000140","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

One of the critical challenges in the big data analytics is the individual's privacy issues. Data anonymization models including k-anonymity and l-diversity are used to guarantee the tradeoff between privacy and data utility while publishing the data. However, these models focus only on the single release of datasets and produce a certain level of privacy. In practical big data applications, data publishing is more complicated where the data is published continuously as new data is collected, and the privacy should be achieved for different releases. In this research, we propose a new distributed bottom up approach on Apache Spark for achievement of the m-invariance privacy model in the continuous big data context. The proposed approach, which is the first study that deals with dynamic big data publishing, is based on the insertion and the split process. In the first process, the data records collected from different workers are inserted into an improved bottom up R-tree generalization in order to minimizing the information loss. The second process concentrates on splitting the overflowed node with respect to the m-invariance model requirement by minimizing the overlap between the resulting partitions. The experimental results show significant improvement in term of data utility, execution time and counterfeit data records as compared to existing techniques in the literature.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Big Data Research
Big Data Research Computer Science-Computer Science Applications
CiteScore
8.40
自引率
3.00%
发文量
0
期刊介绍: The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
期刊最新文献
ImDMI: Improved Distributed M-Invariance model to achieve privacy continuous big data publishing using Apache Spark Modeling meaningful volatility events to classify monetary policy announcements Predicting option prices: From the Black-Scholes model to machine learning methods Editorial Board Efficient training: Federated learning cost analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1