面向数值型敏感属性的分级l-多样性模型

A Multi-Level l-Diversity Model for Numerical Sensitive Attributes

近年来,数据发布隐私保护问题受到了广泛关注,相继提出了多种隐私保护匿名模型.l-多样性模型是其中保护个体隐私的有效方法,但现有的l-多样性模型只适合处理分类型敏感属性,不适合处理数值型敏感属性.为此,提出面向数值型敏感属性的分级l-多样性模型,包括分级相异l-多样性、分级信息熵l-多样性和分级递归(c,l)-多样性.所提出的模型首先将数值型敏感属性域分级,再基于分级信息实现数值型敏感属性的l-多样性.设计了实现这些模型的l-Incognito算法.并且从匿名表的多样性角度进行了比较,实验表明分级l-多样性表比未分级的l-多样性表具有更高的多样度,因此具有更强的抵制同质性攻击和背景知识攻击的能力.

Privacy preservation in data publishing has gained wide concern in databases recently.There are various anonymity models proposed for preserving privacy.The l-diversity is an effective model to preserve individual privacy while publishing data.However,the l-diversity model is suitable for processing categorical sensitive attributes,rather than numerical sensitive attributes,which can not effectively thwart homogeneity attack and background knowledge attack for numerical sensitive attributes.To address this problem,a multi-level l-diversity model based on level distance is proposed especially for numerical sensitive attribute.The main idea of the multi-level l-diversity model is that it divides numerical sensitive values into several levels at first,and then realizes sensitive attribute l-diversity based on these levels and level distance.Instantiations of the multi-level l-diversity model,such as multi-level distinct l-diversity,multi-level l-entropy diversity and multi-level recursive（c,l）-diversity,are introduced.The properties of the multi-level l-diversity model are also analyzed.Based on the properties,an l-incognito algorithm is designed to realize the multi-level l-diversity.Experiments compare the proposed model and the existing l-diversity model in terms of the diversity of anonymity tables.Experimental results show that the anonymity data generated by the l-incognito algorithm on the multi-level l-diversity model have higher sensitive attributes diversity than that on mono-level l-diversity model,so it can resist homogeneity attack and background knowledge attack effectively.