A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation

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.