GSSK: A Generalization Step Safe Algorithm in Anonymizing Data

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 p...

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Published in2010 International Conference on Communications and Mobile Computing Vol. 1; pp. 183 - 187
Main Authors Pin Lv, Yu Wen-bing, Chen Nian-sheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Computer science
Data privacy
Educational institutions
Generalization
Intelligent robots
k-anonymity
Laboratories
Mobile communication
Mobile computing
Protection
Publishing
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ISBN9781424463275
1424463270
DOI10.1109/CMC.2010.191

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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.
AbstractList 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.
Author Pin Lv
Yu Wen-bing
Chen Nian-sheng
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  surname: Chen Nian-sheng
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  email: hschenns@163.com
  organization: Dept. of Comput. Sci., Hubei Normal Coll., Huangshi, China
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Snippet It is necessary to reduce the steps of generalization in order to minimize information loss in privacy preserving data publishing, but sometimes the anonymous...
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StartPage 183
SubjectTerms Computer science
Data privacy
Educational institutions
Generalization
Intelligent robots
k-anonymity
Laboratories
Mobile communication
Mobile computing
Protection
Publishing
Title GSSK: A Generalization Step Safe Algorithm in Anonymizing Data
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