论文标题
对边缘数据完整性验证的全面调查:基本面和未来趋势
A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends
论文作者
论文摘要
边缘计算〜(EC)的最新进展已将基于云的数据缓存服务推向边缘,但是,这种新兴的边缘存储带有许多具有挑战性和独特的安全问题。其中之一是边缘数据完整性验证(EDIV)的问题,该验证(EDIV)协调了多个参与者(例如,数据所有者和边缘节点),以检查在边缘缓存的数据是否真实。迄今为止,已经提出了各种解决方案来解决EDIV问题,而没有系统的审查。因此,我们首次提供了全面的调查,目的是展示当前的研究状况,开放问题以及读者可能有望进一步调查这一不足探索的领域的见解。具体而言,我们首先说明EDIV问题的重要性,云和边缘缓存数据之间的完整性验证差异以及具有相应检查过程的三个典型系统模型。为了彻底评估先前的研究工作,我们综合了一个普遍的标准框架,即有效的验证方法应满足。最重要的是,开发了示意图的时间表,以依次揭示EDIV的研究进展,然后对现有的EDIV解决方案进行详细审查。最后,我们重点介绍了有趣的研究挑战和未来工作的可能方向,以及讨论即将到来的技术,例如机器学习和上下文感知安全性如何可以增强EC中的安全性。鉴于我们的发现,一些主要的观察结果是:为EDIV解决方案配备各种功能并多样化的研究方案存在明显的趋势;在两种类型的参与者(即数据所有者和边缘节点)中完成EDIV正在吸引研究人员的兴趣;尽管大多数现有方法都依赖密码学,但正在探索新兴技术来处理EDIV问题。
Recent advances in edge computing~(EC) have pushed cloud-based data caching services to edge, however, such emerging edge storage comes with numerous challenging and unique security issues. One of them is the problem of edge data integrity verification (EDIV) which coordinates multiple participants (e.g., data owners and edge nodes) to inspect whether data cached on edge is authentic. To date, various solutions have been proposed to address the EDIV problem, while there is no systematic review. Thus, we offer a comprehensive survey for the first time, aiming to show current research status, open problems, and potentially promising insights for readers to further investigate this under-explored field. Specifically, we begin by stating the significance of the EDIV problem, the integrity verification difference between data cached on cloud and edge, and three typical system models with corresponding inspection processes. To thoroughly assess prior research efforts, we synthesize a universal criteria framework that an effective verification approach should satisfy. On top of it, a schematic development timeline is developed to reveal the research advance on EDIV in a sequential manner, followed by a detailed review of the existing EDIV solutions. Finally, we highlight intriguing research challenges and possible directions for future work, along with a discussion on how forthcoming technology, e.g., machine learning and context-aware security, can augment security in EC. Given our findings, some major observations are: there is a noticeable trend to equip EDIV solutions with various functions and diversify study scenarios; completing EDIV within two types of participants (i.e., data owner and edge nodes) is garnering escalating interest among researchers; although the majority of existing methods rely on cryptography, emerging technology is being explored to handle the EDIV problem.