Edge Data Auditing Method Supporting Multi-Keyword Validation

The proliferation of latency-sensitive applications like VR/AR-based immersive gaming, fueled by 5G and edge computing, demands ultra-low latency. While deploying data replicas on edge servers addresses latency, the highly distributed and dynamic edge environment makes these replicas vulnerable to corruption. Furthermore, the constrained resources of edge servers, compared to cloud infrastructure, render traditional data integrity auditing schemes inefficient or impractical. Crucially, existing solutions lack the ability to perform targeted integrity checks on specific data segments (e.g., files containing sensitive user information), leaving critical vulnerabilities undetected. To overcome these limitations, this paper introduces EDI-K, a novel batch auditing scheme featuring multi-keyword authentication. EDI-K's key contributions are: (1) Enabling efficient, targeted integrity verification for data containing specific keywords; (2) Guaranteeing keyword privacy during the auditing process; and (3) Incorporating a novel data structure that facilitates highly efficient dynamic operations (updates, inserts, deletes) on the audited data. Security analysis confirms EDI-K's robustness, while comprehensive performance evaluations demonstrate its significant efficiency advantages over existing approaches, making it particularly suitable for the resource-scarce edge computing landscape.