Big Data Security Hardening Methodology Using Attributes Relationship

Kim, Sunghwan; Eom, Jung-Ho; Chung, Tai-Myoung

doi:10.1109/icisa.2013.6579427

Cited by 12 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The big data study [9] divided big data security into access control, and information security. The proposed security hardening methodology used attribute relation graph, and focused on the data value rather than the data itself.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, big data analysis provides significant opportunities for solving different information security problems by using Hadoop technologies and HDFS tools [8]. The data value that is generated from big data through the analysis phase is of extreme important [9]. However, the intensive use of big data raises new data security challenges [10]- [13], particularly when processing confidential data, such as, organization's trading secrets, personal and health information.…”

Section: Introductionmentioning

confidence: 99%

“…State of the art data mining techniques preserve data privacy by substituting the value of sensitive attributes and forbidding the disclosure of sensitive and private data. However, these techniques cannot fit big data and so cannot address its security and privacy concerns [9]. In addition, data mining faced a challenge when addressing unstructured data types [31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

et al. 2019

View full text Add to dashboard Cite

The expand trend of cloud data mobility led to malicious data threats that necessitate using data protection techniques. Most cloud system applications contain valuable and confidential data, such as personal, trade, or health information. Threats on such data may put the cloud systems that hold these data at high risk. However, traditional security solutions are not capable of handling the security of big data mobility. The current security mechanisms are insufficient for big data due to their shortage of determining the data that should be protected or due to their intractable time complexity. Therefore, the demand for securing mobile big data has been increasing rapidly to avoid any potential risks. This paper proposes an integrated methodology to classify and secure big data before executing data mobility, duplication, and analysis. The necessity of securing big data mobility is determined by classifying the data according to the risk impact level of their contents into two categories; confidential and public. Based on the classification category, the impact of data security is studied and substantiated on the confidential data in the scope of Hadoop Distributed File System. It is revealed that the proposed approach can significantly improve the cloud systems data mobility.INDEX TERMS Big data classification, data security, metadata, risk impact level, HDFS.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

et al. 2019

View full text Add to dashboard Cite

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

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

“… Use attribute relationships for ensuring big data security [24,25].This is another method of securing big data based on relationships among attributes present in big data. The attributes that need to be protected are identified based on type of big data and company policies.…”

mentioning

confidence: 99%

New Design Principles for Effective Knowledge Discovery from Big Data

Gosain¹,

Chugh²

2014

IJCA

View full text Add to dashboard Cite

Big data is creating hype in IT industry. Knowledge discovery from big data can allow organizations to have deeper insights, look at the bigger picture and project big returns. There are various principles that have been presented for knowledge discovery from big data by ORNL (Oak Ridge National University), USA. These are: (i) support a variety of analysis methods, (ii) one size doesn't fit all, (iii) make data accessible. However timeliness and security still pose great challenges in the knowledge discovery process. Timely analysis of big data is essential because data is being produced at a very high velocity. Security of big data is difficult to ensure since big data solutions were not developed with security in mind. In this paper, we give a view of various big data dimensions and present two new principles based on security and timely analysis for knowledge discovery from big data.

show abstract

Big Data Security Hardening Methodology Using Attributes Relationship

Cited by 12 publications

References 5 publications

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

Privacy Preservation in Big Data

New Design Principles for Effective Knowledge Discovery from Big Data

Contact Info

Product

Resources

About