Two privacy-preserving approaches for data publishing with identity reservation

Abstract

Many approaches have been proposed for publishing useful information while preserving data privacy. Among them, the privacy models of identity-reserved (k, l)-anonymity and identity-reserved (α, β) -anonymity have been proposed to handle the situation where an individual could have multiple records. However, the two models fail to prevent attribute disclosure. To this end, we propose two new privacy models: enhanced identity-reserved l-diversity and enhanced identity-reserved (α, β) -anonymity. Moreover, to implement the two privacy models we design a general anonymization algorithm, called DAnonyIR, with clustering technique by calling different decision functions, which can decrease the information loss caused by generalization. Further, we compare DAnonyIR concerning our two privacy models with existing generalization method GeneIR concerning identity-reserved (k, l)-anonymity and identity-reserved (α, β) -anonymity, respectively. The experimental results show that our two approaches provide stronger privacy preservation, and their information loss and relative error ratio of query answering are less than those of GeneIR.