DocumentCode :
3317035
Title :
Frequent Tables for Fast K-Anonymization
Author :
Yang, Xiaochun ; Liu, Xiangyu ; Wang, Bin ; Yu, Ge
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
Volume :
2
fYear :
2006
fDate :
3-6 Nov. 2006
Firstpage :
1239
Lastpage :
1242
Abstract :
K-anonymization is an important approach to protect data privacy in data publishing. When there are multiple constraints, K-anonymizing a table to satisfy all the constraints is much more complex. We propose a K-anonymization approach, FTB-Classfly, which is based on frequent tables. In stead of generalizing all values in an attribute, FTB-Classfly only generalizes partial tuples that do not satisfy the constraints. By using frequent tables, FTB-Classfly provides higher efficiency than existing approaches. Experimental results show that the proposed FTB-Classfly approach can generate a published table more efficiently than other approaches
Keywords :
data privacy; publishing; FTB-Classfly; K-anonymization; data privacy protection; data publishing; frequent tables; Constraint theory; Data engineering; Data privacy; Diseases; Influenza; Information science; Joining processes; Lungs; Protection; Publishing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
Type :
conf
DOI :
10.1109/ICCIAS.2006.295254
Filename :
4076160
Link To Document :
بازگشت