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