DocumentCode :
1924920
Title :
Anonymization technique through record elimination to preserve privacy of published data
Author :
Mahesh, R. ; Meyyappan, T.
Author_Institution :
Dept. of Comput. Sci. & Eng., Alagappa Univ., Karaikudi, India
fYear :
2013
fDate :
21-22 Feb. 2013
Firstpage :
328
Lastpage :
332
Abstract :
The term Data Privacy is associated with data collection and dissemination of data. Privacy issues arise in various area such as health care, intellectual property, biological data etc. It is one of the challenging issues when sharing or publishing the data between one to many sources for research purpose and data analysis. Sensitive information of data owners must be protected. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks Earlier, researchers have proposed new methods namely k-anonymity, l-dlverslty, t-closeness for data privacy. K-anonymity method preserves the privacy against record linkage attack alone. It fails to address attribute linkage attack. l-diversity method overcomes the drawback of k-anonymity method. But it fails to address identity disclosure attack and attribute disclosure attack in some exceptional cases. t-closeness method preserves the privacy against attribute linkage attack but not identity disclosure attack. But it computational complexity is large. In this paper, the authors propose a new method to preserve the privacy of individuals´ sensitive data from record and attribute linkage attacks. In the proposed method, privacy preservation is achieved through generalization of quasi identifier by setting range values and record elimination. The proposed method is implemented and tested with various data sets.
Keywords :
data analysis; data privacy; anonymization technique; attribute linkage attack; biological data; computational complexity; data analysis; data collection; data dissemination; data owner; data privacy; data publishing; data sharing; health care; intellectual property; k-anonymity method; l-dlverslty method; privacy preservation; published data; quasiidentifier generalization; record elimination; record linkage attack; t-closeness method; Couplings; Data privacy; Diseases; Lungs; Privacy; Publishing; Remuneration; anonymization; data mining; data privacy; data publishing; privacy preservation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on
Conference_Location :
Salem
Print_ISBN :
978-1-4673-5843-9
Type :
conf
DOI :
10.1109/ICPRIME.2013.6496495
Filename :
6496495
Link To Document :
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