A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends

TL;DR

This paper surveys the edge data integrity verification (EDIV) problem, defining its unique challenges in edge environments where data owners outsource replicas to edge nodes. It introduces three system models—private audit, public audit, and cooperative audit—and a universal criteria framework to evaluate EDIV approaches in terms of efficiency, security, and functionality. Through a development timeline and taxonomy, the survey catalogues current EDIV solutions, assesses their strengths and limitations, and identifies open problems such as dynamic data handling, data recovery, privacy, and multi‑owner scenarios. The discussion points to future directions, including the integration of AI, context‑aware security, and secure hardware to enhance EDIV’s practicality and resilience in real‑world edge deployments.

Abstract

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.

Figures (9)

• Figure 1: Example of edge storage. A data owner caches multiple data replicas to geographically distributed edge nodes (denoted by $S_1$, $S_2$, $S_3$) to serve nearby data users (denoted by $u_i,\ i\in \{1,2,\cdots,9\}$) with ultra-low data access latency.
• Figure 2: Roadmap of the survey
• Figure 3: A comparative visual summary between edge data integrity verification and cloud data integrity verification
• Figure 4: System models. There are three types of system models: (1) private audit where data owner/user interacts with edge nodes; (2) public audit where TPA interacts with edge nodes; and (3) cooperative audit involving interactions among edge nodes.
• Figure 5: Evaluation criteria of edge data integrity verification solutions

Theorems & Definitions (1)

• Definition 1: Edge Data Integrity Verification Problem