Enhanced Additive Noise Approach For Privacy-Preserving Tabular Data Publishing

Abstract

With the recent remarkable and fast evolution in telecommunication and computing technologies, great amounts of individuals' tabular-formatted data are collected and used by several organizations in the society. In some cases, some organizations need to share these gathered data to be used in business analysis, decision making or scientific researches purposes, which can involve sensitive information about the individuals. However, these data cannot be published in their original form to other third parties due to the associated privacy concerns. Consequently, preserving individuals' privacy represents a critical issue when sharing the individuals' private data. Hence, Privacy-Preserving Tabular Data Publishing (PPTDP) has received a great attention to protect the privacy of individuals' tabular data, where several approaches have been presented to address this issue. In this paper, we propose an enhanced additive noise approach for privacy-preserving microdata with Single Sensitive Attribute (SSA) publishing. The proposed approach maintains better published data utility to allow more accurate mining and analytical results, where more robust privacy protection against privacy attacks is provided.