A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions
Ishu Gupta, Ashutosh Kumar Singh

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…
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Taxonomy
TopicsDigital and Cyber Forensics · Privacy-Preserving Technologies in Data · Cloud Data Security Solutions
