Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users’ data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors’ cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.

中文翻译：

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审核边缘计算环境中的缓存数据完整性

边缘计算允许应用程序供应商在分布式边缘服务器上部署他们的应用程序和相关数据，为附近的用户提供服务。在边缘服务器上缓存数据可以最大限度地减少用户的数据检索延迟。然而，在高度分布式、动态和易变的边缘计算环境中，此类缓存数据容易受到故意和意外损坏。鉴于边缘服务器数量众多且计算资源有限，如何有效且高效地审计应用供应商缓存数据的完整性是一个关键且具有挑战性的问题。本文首次尝试解决此边缘数据完整性 (EDI) 问题。我们首先分析威胁模型和审计目标，然后提出一种基于轻量级抽样的概率方法，即 EDI-V，帮助应用程序供应商审核其在大规模边缘服务器上缓存的数据的完整性。我们提出了一种名为可变默克尔哈希树 (VMHT) 的新数据结构，用于在审计期间生成这些数据副本的完整性证明。VMHT 可以通过保持采样的一致性来保证 EDI-V 的审计准确性。EDI-V 允许应用程序供应商检查他们的缓存数据并高效地定位损坏的数据。理论分析和综合实验评估都证明了EDI-V的效率和有效性。EDI-V 允许应用程序供应商检查他们的缓存数据并高效地定位损坏的数据。理论分析和综合实验评估都证明了EDI-V的效率和有效性。EDI-V 允许应用程序供应商检查他们的缓存数据并高效地定位损坏的数据。理论分析和综合实验评估都证明了EDI-V的效率和有效性。