Data Privacy in Multi-Cloud: An Enhanced Data Fragmentation Framework

TL;DR

This paper introduces an improved data fragmentation framework for multi-cloud environments that reduces resource use by leveraging existing data fragments, minimizing splitting operations and storage management.

Contribution

It proposes a novel framework that utilizes existing multi-cloud data to optimize data splitting, enhancing efficiency and reducing resource consumption.

Findings

Reduces number of splitting operations

Decreases storage locations managed by data owners

Enhances existing data splitting techniques

Abstract

Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear as opposed to methods that rely on cryptography. However, majority of existing data splitting techniques do not consider data already in the multi-cloud. This leads to unnecessary use of resources to re-split data into fragments. This work proposes a data splitting framework that leverages on existing data in the multi-cloud. It improves data splitting mechanisms by reducing the number of splitting operations and resulting fragments. Therefore, decreasing the number of storage locations a data owner manages. Broadcasts queries locate third-party data fragments to avoid costly operations when splitting data. This work examines considerations for the use of third-party fragments and application to existing data splitting techniques. The proposed framework was also applied to an existing data splitting mechanism to complement its capabilities.