网络边缘隐私保护缓存研究:分类、解决方案和挑战

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-12-10 DOI:10.1145/3706630
Xianzhi Zhang, Yipeng Zhou, Di Wu, Quan Z. Sheng, Shazia Riaz, Miao Hu, Linchang Xiao
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引用次数: 0

摘要

边缘网络内容缓存技术是一种广泛应用的有效技术,可以减轻网络回程负担,缩短业务延迟,提高业务质量。然而,在边缘网络中缓存内容侵犯隐私的问题上存在一些争议。一方面,多访问开放边缘网络为外部攻击者通过提取敏感信息从边缘缓存中获取私有数据提供了理想的入口或接口。另一方面,好奇的边缘缓存提供商可能会通过缓存跟踪分析来侵犯隐私,目的是实现更好的缓存性能或更高的利润。因此,深入了解边缘缓存网络中的隐私问题对于在边缘网络中创建保护隐私的缓存服务至关重要。在本文中,我们将通过研究在边缘网络中缓存内容的隐私保护技术来填补这一空白。首先,我们介绍了隐私保护边缘缓存(PPEC)的背景。接下来,我们总结了关键的隐私问题,并从隐私信息的角度提出了边缘网络缓存的分类。此外,我们对防止边缘网络中内容缓存的隐私泄露的最新对策进行了回顾性审查。最后,我们总结了调查并展望了未来研究的挑战。
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A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations in caching content at the edge network. On the one hand, the multi-access open edge network provides an ideal entrance or interface for external attackers to obtain private data from edge caches by extracting sensitive information. On the other hand, privacy can be infringed on by curious edge caching providers through caching trace analysis targeting the achievement of better caching performance or higher profits. Therefore, an in-depth understanding of privacy issues in edge caching networks is vital and indispensable for creating a privacy-preserving caching service at the edge network. In this article, we are among the first to fill this gap by examining privacy-preserving techniques for caching content at the edge network. Firstly, we provide an introduction to the background of privacy-preserving edge caching (PPEC). Next, we summarize the key privacy issues and present a taxonomy for caching at the edge network from the perspective of private information. Additionally, we conduct a retrospective review of the state-of-the-art countermeasures against privacy leakage from content caching at the edge network. Finally, we conclude the survey and envision challenges for future research.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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