Volume-Hiding Range Searchable Symmetric Encryption for Large-Scale Datasets

IF 4.4 2区 化学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Feng Liu, Kaiping Xue, Jinjiang Yang, Jing Zhang, Zixuan Huang, Jian Li, David S. L. Wei
{"title":"Volume-Hiding Range Searchable Symmetric Encryption for Large-Scale Datasets","authors":"Feng Liu, Kaiping Xue, Jinjiang Yang, Jing Zhang, Zixuan Huang, Jian Li, David S. L. Wei","doi":"10.1109/TDSC.2023.3335304","DOIUrl":null,"url":null,"abstract":"Searchable Symmetric Encryption (SSE) is a valuable cryptographic tool that allows a client to retrieve its outsourced data from an untrusted server via keyword search. Initially, SSE research primarily focused on the efficiency-security trade-off. However, in recent years, attention has shifted towards range queries instead of exact keyword searches, resulting in significant developments in the SSE field. Despite the advancements in SSE schemes supporting range queries, many are susceptible to leakage-abuse attacks due to volumetric profile leakage. Although several schemes exist to prevent volume leakage, these solutions prove inefficient when dealing with large-scale datasets. In this article, we highlight the efficiency-security trade-off for range queries in SSE. Subsequently, we propose a volume-hiding range SSE scheme that ensures efficient operations on extensive datasets. Leveraging the order-weighted inverted index and bitmap structure, our scheme achieves high search efficiency while maintaining the confidentiality of the volumetric profile. To facilitate searching within large-scale datasets, we introduce a partitioning strategy that divides a broad range into disjoint partitions and stores the information in a local binary tree. Through an analysis of the leakage function, we demonstrate the security of our proposed scheme within the ideal/real model simulation paradigm. Our experimental results further validate the practicality of our scheme with real-life large-scale datasets.","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":"23 1","pages":"3597-3609"},"PeriodicalIF":4.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Polymer Materials","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/TDSC.2023.3335304","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Searchable Symmetric Encryption (SSE) is a valuable cryptographic tool that allows a client to retrieve its outsourced data from an untrusted server via keyword search. Initially, SSE research primarily focused on the efficiency-security trade-off. However, in recent years, attention has shifted towards range queries instead of exact keyword searches, resulting in significant developments in the SSE field. Despite the advancements in SSE schemes supporting range queries, many are susceptible to leakage-abuse attacks due to volumetric profile leakage. Although several schemes exist to prevent volume leakage, these solutions prove inefficient when dealing with large-scale datasets. In this article, we highlight the efficiency-security trade-off for range queries in SSE. Subsequently, we propose a volume-hiding range SSE scheme that ensures efficient operations on extensive datasets. Leveraging the order-weighted inverted index and bitmap structure, our scheme achieves high search efficiency while maintaining the confidentiality of the volumetric profile. To facilitate searching within large-scale datasets, we introduce a partitioning strategy that divides a broad range into disjoint partitions and stores the information in a local binary tree. Through an analysis of the leakage function, we demonstrate the security of our proposed scheme within the ideal/real model simulation paradigm. Our experimental results further validate the practicality of our scheme with real-life large-scale datasets.
大规模数据集的卷隐藏范围可搜索对称加密
可搜索对称加密(SSE)是一种有价值的加密工具,它允许客户通过关键字搜索从不受信任的服务器检索其外包数据。最初,SSE 的研究主要集中在效率与安全的权衡上。然而,近年来,人们的注意力已从精确的关键字搜索转向范围查询,从而推动了 SSE 领域的重大发展。尽管支持范围查询的 SSE 方案取得了进步,但许多方案仍容易因体积特征泄漏而受到滥用泄漏攻击。虽然有几种方案可以防止体积泄漏,但在处理大规模数据集时,这些方案被证明效率低下。在本文中,我们将重点讨论 SSE 中范围查询的效率-安全权衡问题。随后,我们提出了一种体积隐藏范围的 SSE 方案,它能确保在大规模数据集上的高效操作。利用阶次加权倒排索引和位图结构,我们的方案在实现高搜索效率的同时,还能保持体积轮廓的机密性。为了便于在大规模数据集中进行搜索,我们引入了一种分区策略,将广泛的范围划分为不相连的分区,并将信息存储在本地二叉树中。通过对泄漏函数的分析,我们在理想/真实模型模拟范例中证明了我们提出的方案的安全性。我们的实验结果进一步验证了我们的方案在现实生活大规模数据集中的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.20
自引率
6.00%
发文量
810
期刊介绍: ACS Applied Polymer Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics, and biology relevant to applications of polymers. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates fundamental knowledge in the areas of materials, engineering, physics, bioscience, polymer science and chemistry into important polymer applications. The journal is specifically interested in work that addresses relationships among structure, processing, morphology, chemistry, properties, and function as well as work that provide insights into mechanisms critical to the performance of the polymer for applications.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信