Title :
A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
Author :
Jian-min, Han ; Hui-qun, Yu ; Juan, Yu ; Ting-ting, Cen
Author_Institution :
Dept. of Comput. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai
Abstract :
General (alpha,k)-anonymity model is an effective approach to protecting individual privacy before microdata are released. But it has some defects on privacy preservation and data distortion when the distribution of sensitive values is not well-proportioned. To solve the problem, a complete (alpha,k)-anonymity model is proposed which can implement sensitive values´ individuation preservation by setting the frequency constraints for each sensitive value in all the equivalence classes. The relationship of complete (alpha,k)-anonymity model with k-anonymity, simple (alpha,k)-anonymity model and general (alpha,k)-anonymity model is indicated. The paper also investigates distance measurement between tuples and between equivalence classes in generalization trees, and based on the measurement, a complete (alpha,k)-anonymity clustering algorithm is proposed. Experimental results show that the complete (alpha,k)-anonymity model preserves privacy effectively with less data distortion.
Keywords :
data privacy; equivalence classes; trees (mathematics); complete (alpha,k)-anonymity clustering algorithm; complete (alpha,k)-anonymity model; data distortion; distance measurement; equivalence classes; frequency constraints; generalization trees; individual privacy; k-anonymity; microdata; sensitive values individuation preservation; Acquired immune deficiency syndrome; Clustering algorithms; Computer science; Data privacy; Educational institutions; Frequency; Influenza; Physics; Protection; Resists; (a; Homogeneity Attack; K-anonymity; k)-Anonymity Model; l-Diversity;
Conference_Titel :
Electronic Commerce and Security, 2008 International Symposium on
Conference_Location :
Guangzhou City
Print_ISBN :
978-0-7695-3258-5
DOI :
10.1109/ISECS.2008.92