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Fortifying Big Data infrastructures to Face Security and Privacy Issues

Journal from gdlhub / 2016-11-14 09:32:32
Oleh : Tole Sutikno, Deris Stiawan, Imam Much Ibnu Subroto, Telkomnika
Dibuat : 2014-12-01, dengan 1 file

Keyword : Fortifying Big Data infrastructures Face Security Privacy Issues
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/957

The explosion of data available on the internet is very increasing in recent years. One of the most challenging issues is how to effectively manage such a large amount of data and identify new ways to analyze large amounts of data and unlock information. Organizations must find a way to manage their data in accordance with all relevant privacy regulations without making the data inaccessible and unusable. Cloud Security Alliance (CSA) has released that the top 10 challenges, which are as follows: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transactions logs, 4) end-point input validation/filtering, 5) real-time security monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced data centric security, 8) granular access control, 9) granular audits, 10) data Provenance. The challenges themselves can be organized into four distinct aspects of the Big Data ecosystem.

Deskripsi Alternatif :

The explosion of data available on the internet is very increasing in recent years. One of the most challenging issues is how to effectively manage such a large amount of data and identify new ways to analyze large amounts of data and unlock information. Organizations must find a way to manage their data in accordance with all relevant privacy regulations without making the data inaccessible and unusable. Cloud Security Alliance (CSA) has released that the top 10 challenges, which are as follows: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transactions logs, 4) end-point input validation/filtering, 5) real-time security monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced data centric security, 8) granular access control, 9) granular audits, 10) data Provenance. The challenges themselves can be organized into four distinct aspects of the Big Data ecosystem.

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