Survey of Techniques on Data Leakage Protection and Methods to address the Insider threat

• Published in: Cluster computing 2022-12, Vol.25 (6), p.4289-4302
• Main Authors: Herrera Montano, Isabel, García Aranda, José Javier, Ramos Diaz, Juan, Molina Cardín, Sergio, de la Torre Díez, Isabel, Rodrigues, Joel J. P. C.
• Format: Article
• Language: English
• Subjects: Algorithms; Biometrics; Blockchain; Computer Communication Networks; Computer Science; Copy protection; Cryptography; Data integrity; Digital rights management; Employees; Leakage; Literature reviews; Machine learning; Operating Systems; Processor Architectures; Systematic review
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-022-03668-2

Data leakage is a problem that companies and organizations face every day around the world. Mainly the data leak caused by the internal threat posed by authorized personnel to manipulate confidential information. The main objective of this work is to survey the literature to detect the existing techniques to protect against data leakage and to identify the methods used to address the insider threat. For this, a literature review of scientific databases was carried out in the period from 2011 to 2022, which resulted in 42 relevant papers. It was obtained that from 2017 to date, 60% of the studies found are concentrated and that 90% come from conferences and publications in journals. Significant advances were detected in protection systems against data leakage with the incorporation of new techniques and technologies, such as machine learning, blockchain, and digital rights management policies. In 40% of the relevant studies, significant interest was shown in avoiding internal threats. The most used techniques in the analyzed DLP tools were encryption and machine learning.