DocumentCode :
517411
Title :
GSSK: A Generalization Step Safe Algorithm in Anonymizing Data
Author :
Pin Lv ; Yu Wen-bing ; Chen Nian-sheng
Author_Institution :
Sch. of Comput. Sci. & Eng., Wuhan Inst. of Technol., Wuhan, China
Volume :
1
fYear :
2010
fDate :
12-14 April 2010
Firstpage :
183
Lastpage :
187
Abstract :
It is necessary to reduce the steps of generalization in order to minimize information loss in privacy preserving data publishing, but sometimes the anonymous table on basis of the method could still be attacked. To solve the problem, the condition of attack is analyzed, and a m-threshold model is presented to decide whether the value of quasi-identifier attribute would be continuously generalized, making use of algorithm of SSGK dealing with the model. Computer experiments show that the GSSK algorithm can prevent the attack with little information loss.
Keywords :
data handling; data privacy; publishing; data anonymity; generalization step safe algorithm; information loss minimization; m-threshold model; privacy preserving data publishing; quasiidentifier attribute; Computer science; Data privacy; Databases; Educational institutions; Intelligent robots; Laboratories; Mobile communication; Mobile computing; Protection; Publishing; Data Privacy; Generalization; k-anonymity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications and Mobile Computing (CMC), 2010 International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-6327-5
Electronic_ISBN :
978-1-4244-6328-2
Type :
conf
DOI :
10.1109/CMC.2010.191
Filename :
5471489
Link To Document :
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