ε-inclusion: privacy preserving re-publication of dynamic datasets

This paper presents a novel privacy principle,ε-inclusion,for re-publishing sensitive dynamic datasets,ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata.Combined with generalization-based methods,ε-inclusion protects privacy and captures a large amount of correlation in the microdata.We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement.Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.