Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

TL;DR

The paper introduces ${ ext{O}^2 ext{DI}}$, a novel data inspection method for edge computing that enables efficient, secure, and batch verification of cached data without requiring original data, addressing resource constraints and security concerns.

Contribution

The paper presents ${ ext{O}^2 ext{DI}}$, a new online/offline, batch verification approach for data inspection in edge computing, improving efficiency and security over existing methods.

Findings

${ ext{O}^2 ext{DI}}$ reduces I/O and storage overhead.

It enables quick batch verification of data files.

It is secure in the random oracle model and more cost-effective.

Abstract

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named ${\text{O}^2\text{DI}}$, that provides the following services: i) using ${\text{O}^2\text{DI}}$, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly; ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique; iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method; iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of ${\text{O}^2\text{DI}}$. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches.