A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions

TL;DR

This paper provides a comprehensive overview of data protection strategies across various environments, discussing taxonomy, challenges, and future research directions, with a focus on machine learning-based solutions for safeguarding organizational data.

Contribution

It offers a holistic taxonomy of data leakage protection systems and analyzes state-of-the-art machine learning approaches, highlighting future challenges in data security.

Findings

Taxonomy of data leakage protection systems

Analysis of machine learning-based data security solutions

Identification of emerging challenges and future research directions

Abstract

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System's analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection.