A Forecasting-Based DLP Approach for Data Security

Gupta, Kishu; Kush, Ashwani

doi:10.1007/978-981-15-8335-3_1

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…A Forecasting-Based DLP Approach for Data Security [51] A DLP solution is presented that allows classification of users accessing confidential information according to the number of accesses, based on past data, to predict the future through statistical models.…”

Section: Encryptionmentioning

confidence: 99%

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

Montano

Aranda²,

Diaz³

et al. 2022

Cluster Comput

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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.

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Section: Encryptionmentioning

confidence: 99%

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

Montano

Aranda²,

Diaz³

et al. 2022

Cluster Comput

View full text Add to dashboard Cite

show abstract

MAIDS: malicious agent identification-based data security model for cloud environments

Gupta,

Saxena,

Gupta

et al. 2024

Cluster Comput

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A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

Mishra,

Gupta,

Saxena

et al. 2024

IEEE Trans. Consumer Electron.

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Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs.

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A Forecasting-Based DLP Approach for Data Security

Cited by 4 publications

References 12 publications

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

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

MAIDS: malicious agent identification-based data security model for cloud environments

A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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