Title :
Big Data Security Hardening Methodology Using Attributes Relationship
Author :
Sung-Hwan Kim ; Jung-Ho Eom ; Tai-Myoung Chung
Author_Institution :
Sch. of Inf. Commun. Eng., Sungkyunkwan Univ., Suwon, South Korea
Abstract :
Recently developments in network, mining and data store technology have heightened the need for big data and big data security. In this paper, we focus on the big data\´s characteristic which takes seriously the analysis of value than the data itself. We express the relationship between attributes using nodes and edges. Through this, we propose a big data security hardening methodology by selecting "protect attributes" from attributes relationship graph.
Keywords :
data mining; graph theory; security of data; storage management; attributes relationship graph; big data security hardening methodology; data store technology; edges; mining technology; nodes; protect attribute selection; Data handling; Data mining; Data storage systems; Databases; Educational institutions; Information management; Security;
Conference_Titel :
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location :
Suwon
Print_ISBN :
978-1-4799-0602-4
DOI :
10.1109/ICISA.2013.6579427