DocumentCode :
3539872
Title :
Hybrid framework for privacy preserving data sharing
Author :
Abeysekara, Ruvan Kumara ; Weishi Zhang
Author_Institution :
Dalian Maritime Univ., Dalian, China
fYear :
2013
fDate :
11-15 Dec. 2013
Firstpage :
198
Lastpage :
206
Abstract :
Privacy preserving data mining has become increasingly popular and continuously evolving field of study. It allows sharing of privacy sensitive data for analysis purposes. The recent advancement in data mining technology to analyze vast amount of data has played an important role in several areas of Business processing. Data mining also opens new threats to privacy and information security if not done or used properly. Therefore this research elaborates and introduces new Hybrid Algorithm for Privacy Preserving Data Sharing. It opens the gates to touch finer points of Hybrid methodologies in privacy preserving data mining. Experiments based on the discussions of literature, mainly about data sanitization done to prove the set of hypothesis mentioned on this paper.
Keywords :
data analysis; data mining; data privacy; business processing; data analysis; data mining technology; hybrid framework; hybrid methodologies; information security; privacy preserving data mining; privacy preserving data sharing; privacy sensitive data sharing; Association rules; Data models; Data privacy; Databases; Measurement; Privacy; Data mining; Data mining Algorithms; Data sanitization; Privacy preserving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in ICT for Emerging Regions (ICTer), 2013 International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4799-1275-9
Type :
conf
DOI :
10.1109/ICTer.2013.6761179
Filename :
6761179
Link To Document :
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