PFDup:加密多媒体数据的实用模糊重复数据删除

IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Industrial Information Integration Pub Date : 2024-04-17 DOI:10.1016/j.jii.2024.100613
Shuai Cheng , Zehui Tang , Shengke Zeng , Xinchun Cui , Tao Li
{"title":"PFDup:加密多媒体数据的实用模糊重复数据删除","authors":"Shuai Cheng ,&nbsp;Zehui Tang ,&nbsp;Shengke Zeng ,&nbsp;Xinchun Cui ,&nbsp;Tao Li","doi":"10.1016/j.jii.2024.100613","DOIUrl":null,"url":null,"abstract":"<div><p>Redundant data wastes cloud storage space, especially the multimedia data which comprises a large number of similar files and accounts for the majority of cloud storage. To protect privacy and eliminate redundancy in the cloud, fuzzy deduplication for encrypted multimedia data is practical and feasible. Unfortunately, existing fuzzy deduplications depend on aided server to be against security threats. In this paper, we propose a Practical Fuzzy Deduplication (PFDup) algorithm for encrypted multimedia data and it is secure against brute-force guessing attacks without additional independent severs. With our secure fuzzy deduplication technology, cloud storage can be significantly optimized by using Perceptual Hash (phash) to eliminate large quantities of identical even the similar multimedia data in a secure manner. In addition, PFDup protocol supports label consistency and a non-interactive Proof of Ownership (PO) in order to prevent the server–client collusion attacks. We conduct a series of experiments on numerous real-world datasets and the simulation results show that our deduplication rate for the similar images is over 91.5%.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"40 ","pages":"Article 100613"},"PeriodicalIF":10.4000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PFDup: Practical Fuzzy Deduplication for Encrypted Multimedia Data\",\"authors\":\"Shuai Cheng ,&nbsp;Zehui Tang ,&nbsp;Shengke Zeng ,&nbsp;Xinchun Cui ,&nbsp;Tao Li\",\"doi\":\"10.1016/j.jii.2024.100613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Redundant data wastes cloud storage space, especially the multimedia data which comprises a large number of similar files and accounts for the majority of cloud storage. To protect privacy and eliminate redundancy in the cloud, fuzzy deduplication for encrypted multimedia data is practical and feasible. Unfortunately, existing fuzzy deduplications depend on aided server to be against security threats. In this paper, we propose a Practical Fuzzy Deduplication (PFDup) algorithm for encrypted multimedia data and it is secure against brute-force guessing attacks without additional independent severs. With our secure fuzzy deduplication technology, cloud storage can be significantly optimized by using Perceptual Hash (phash) to eliminate large quantities of identical even the similar multimedia data in a secure manner. In addition, PFDup protocol supports label consistency and a non-interactive Proof of Ownership (PO) in order to prevent the server–client collusion attacks. We conduct a series of experiments on numerous real-world datasets and the simulation results show that our deduplication rate for the similar images is over 91.5%.</p></div>\",\"PeriodicalId\":55975,\"journal\":{\"name\":\"Journal of Industrial Information Integration\",\"volume\":\"40 \",\"pages\":\"Article 100613\"},\"PeriodicalIF\":10.4000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Industrial Information Integration\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452414X24000578\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Information Integration","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452414X24000578","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

冗余数据会浪费云存储空间,尤其是由大量相似文件组成的多媒体数据,占云存储的绝大部分。为了保护隐私并消除云中的冗余数据,对加密多媒体数据进行模糊重复数据删除是切实可行的。遗憾的是,现有的模糊重复数据删除依赖于辅助服务器来抵御安全威胁。在本文中,我们提出了一种针对加密多媒体数据的实用模糊重复数据删除算法(PFDup),该算法无需额外的独立隔离装置即可安全地抵御暴力猜测攻击。利用我们的安全模糊重复数据删除技术,云存储可以通过使用感知哈希(phash)以安全的方式消除大量相同甚至相似的多媒体数据,从而大大优化云存储。此外,PFDup 协议还支持标签一致性和非交互式所有权证明(PO),以防止服务器-客户端串通攻击。我们在大量真实数据集上进行了一系列实验,模拟结果表明,我们对相似图像的重复数据删除率超过 91.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
PFDup: Practical Fuzzy Deduplication for Encrypted Multimedia Data

Redundant data wastes cloud storage space, especially the multimedia data which comprises a large number of similar files and accounts for the majority of cloud storage. To protect privacy and eliminate redundancy in the cloud, fuzzy deduplication for encrypted multimedia data is practical and feasible. Unfortunately, existing fuzzy deduplications depend on aided server to be against security threats. In this paper, we propose a Practical Fuzzy Deduplication (PFDup) algorithm for encrypted multimedia data and it is secure against brute-force guessing attacks without additional independent severs. With our secure fuzzy deduplication technology, cloud storage can be significantly optimized by using Perceptual Hash (phash) to eliminate large quantities of identical even the similar multimedia data in a secure manner. In addition, PFDup protocol supports label consistency and a non-interactive Proof of Ownership (PO) in order to prevent the server–client collusion attacks. We conduct a series of experiments on numerous real-world datasets and the simulation results show that our deduplication rate for the similar images is over 91.5%.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Industrial Information Integration
Journal of Industrial Information Integration Decision Sciences-Information Systems and Management
CiteScore
22.30
自引率
13.40%
发文量
100
期刊介绍: The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers. The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.
期刊最新文献
Enhancing mixed gas discrimination in e-nose system: Sparse recurrent neural networks using transient current fluctuation of SMO array sensor An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security Industrial information integration in deep space exploration and exploitation: Architecture and technology Interoperability levels and challenges of digital twins in cyber–physical systems
×
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