Attribute Relationship Evaluation Methodology for Big Data Security

Kim, Sunghwan; Kim, Nam-Uk; Chung, Tai-Myoung

doi:10.1109/icitcs.2013.6717808

Cited by 48 publications

(18 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Attribute based encryption can also be used for big data privacy [11,12]. This method of securing big data is based on relationships among attributes present in big data.…”

Section: Traditional Data Privacy Preservation Methodsmentioning

confidence: 99%

Privacy Preservation in Big Data

Gosain¹,

Chugh²

2014

IJCA

View full text Add to dashboard Cite

Big data has brought a revolution in the world of data analytics. Data that was discarded a few years back is now considered a powerful asset. Big data is now being extensively used for knowledge discovery by all sectors of society. It is produced by almost all digital processes and is stored, shared on web. This reliance of big data on web model poses serious security concerns. Traditional security methods cannot be applied to big data due to its large volume, variety and volume. Also since big data contains person specific information, privacy is a major security concern. The three important privacy preservation methods are: data anonymization, notice and consent and differential privacy. In this paper we discuss these privacy preservation methods for big data and how differential privacy is a better solution for big data privacy.

show abstract

“…Attribute based encryption can also be used for big data privacy [11,12]. This method of securing big data is based on relationships among attributes present in big data.…”

Section: Traditional Data Privacy Preservation Methodsmentioning

confidence: 99%

Privacy Preservation in Big Data

Gosain¹,

Chugh²

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…However, it does not support or tackle the issue of data classification; i.e., it does not discuss handling different data types such as images, regular documents, tables, and realtime information (e.g., VoIP communications). In [3], the authors investigated the security issues encountered by big data when used in cloud networks. The main issues covered by this work are network security, information security, and privacy.…”

Section: Related Workmentioning

confidence: 99%

“…Potential challenges for big data handling consist of the following elements [3]: (i) Analysis: this process focuses on capturing, inspecting, and modeling of data in order to extract useful information.…”

Section: Introductionmentioning

confidence: 99%

An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

Alouneh

Al‐Hawari

Hababeh

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers. The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time.

show abstract

“…we have studied various forms of big data analytical techniques which 34 are associated with potential applications of big data such as patients records in big organizations, breaching cannot be completely removed. Figure 2 elaborates the details of the architecture which mitigate and minimize the breaching problems.…”

Section: Mitigation Of Big Data Breachesmentioning

confidence: 99%

Analytical Techniques for Decision Making on Information Security for Big Data Breaches

Albeshri

Thayananthan

2018

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

In the big data processes, management and analytics are primary areas where we can introduce the decision making on information security to mitigate the big data breaches. According to the growing number of online systems and big data handling, mitigating the big data breaches is the serious problem during the processing period which needs to be monitored using appropriate technique. The goal of this research is to prevent the big data breaches using correct decision making based on information security concepts such as access control with authentication which depend on the management policies. The analytical approach of information security solution can also be useful for securing the big data infrastructure and key management that improve the big data breaches. As an analytical method, information security which focuses on detecting and securing the big data breaches is considered with access control. Here, we have introduced the multi-priority model in°uenced with the network calculus and access control which monitors the breaches during the big data processing. In the results and analysis, we can provide a graph which shows the monitoring improvement for decision making during the mitigation of big data breaches.

show abstract

Attribute Relationship Evaluation Methodology for Big Data Security

Cited by 48 publications

References 6 publications

Privacy Preservation in Big Data

Privacy Preservation in Big Data

An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks

Analytical Techniques for Decision Making on Information Security for Big Data Breaches

Contact Info

Product

Resources

About