DocumentCode
3717345
Title
Toward big data risk analysis
Author
Ernesto Damiani
Author_Institution
Etisalat British Telecom Innovation Center, Khalifa University of Science, Technology and Research, Abu Dhabi, UAE
fYear
2015
Firstpage
1905
Lastpage
1909
Abstract
The advent of social networks and Internet-of-Things has resulted in unprecedented capability of collecting, sharing and analyzing massive amounts of data. From a security perspective, Big Data may seriously weaken confidentiality, as techniques for improving Big Data analytics performance-including early fusion of heterogeneous data sources - increase the hidden redundancy of data representation, generating ill-protected copies. This gray area of redundancy triggers new disclosure threats that challenge traditional techniques to protect privacy and confidentiality. This position paper starts by proposing a definition of the Big Data Leak threat (as opposed to the one of data breach) and its role as a component of disclosure risk. Then, it discusses how a paradigm of Known, Detect, Contain and Recover could be used to establish Big Data security practices for containing disclosure risks connected to Big Data analytics.
Keywords
"Big data","Data models","Redundancy","Security","Companies","ISO Standards","Taxonomy"
Publisher
ieee
Conference_Titel
Big Data (Big Data), 2015 IEEE International Conference on
Type
conf
DOI
10.1109/BigData.2015.7363966
Filename
7363966
Link To Document