Privacy Preserving Data Publishing through Slicing

Abstract

Microdata publishing should be privacy preserved as it may contain some sensitive information about an individual. Various anonymization techniques, generalization and bucketization, have been designed for privacy preserving microdata publishing. Generalization does not work better for high dimensional data. Bucketization failed to prevent membership disclosure and does not show a clear separation between quasi-identifiers and sensitive attributes. There are number of attributes in each record which can be categorized as 1) Identifiers such as Name or Social Security Number are the attributes that can be uniquely identify the individuals. 2)Some attributes may be Sensitive Attributes(SAs) such as disease and salary and 3) Some may be Quasi Identifiers (QI) such as zipcode, age, and sex whose values, when taken together, can potentially identify an individual. Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. Here, we present a novel technique called slicing which partitions the data both horizontally and vertically. It preserves better data utility than generalization and is more effective than bucketization in terms of sensitive attribute.