Multi-level personalized k-anonymity privacy-preserving model based on sequential three-way decisions

Abstract

K-anonymity is a widely used privacy-preserving technique which defends against linking attacks by suppression and generalization. The existing k-anonymity algorithms prevent attackers from illegally obtaining private information by constraining at least k records in an equivalence group. However, this unified anonymity method ignores individual differences and leads to a large amount of information loss. To this end, we introduce sequential three-way decisions into k-anonymity, using a dynamic k-value sequence instead of the fixed k-value to achieve personalized k-anonymity. Specifically, we first construct a hierarchical decision table for k-anonymity by attribute generalization trees and sensitive decision values provided with users. Then, we propose a multi-level personalized k-anonymity privacy-preserving model based on sequential three-way decisions, where we anonymize the partitioning granular data with a dynamic k-value sequence, respectively. Furthermore, we present three practical algorithms to implement the proposed model and discuss the differences between them. Finally, the experimental results demonstrate that the proposed model not only provides a more flexible anonymization method to achieve personalized anonymity, but greatly reduces the information loss. This study provides a complete framework for multi-level privacy protection and enriches the application of sequential three-way decisions.