Auditing Cache Data Integrity in the Edge Computing Environment

Abstract

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.