Background Knowledge Attacks in Privacy-Preserving Data Publishing Models

Abstract

Massive volumes of data are being generated at every moment through various sources in the cyber-physical world. While storing as well as facilitating these data for business or individual requirements, data disclosure, sensitive data leakage, and privacy breaches are important concerns to both service providers and service consumers. Many privacy-preserving data publishing models came into existence to protect data security and privacy from disclosure. Background knowledge has been an important data source to the adversary and has become a potential threat to many privacy-preserving data publishing models. Background knowledge allows the adversary to reveal sensitive information of an individual from the published data. In this paper, we formalize background knowledge by defining different knowledge sets. We present a privacy model against the given background knowledge. We analyze the conventional privacy-preserving data publishing models such as k-anonymity, l-diversity, and t-closeness against the background knowledge attacks and show that all these privacy models fail to preserve privacy against the comprehensive background knowledge adversarial model, which we formalized in this work. Comprehensive background knowledge attacks in privacy-preserving data publishing models are practical in many real-world applications, and we believe that the privacy model presented in this work advances the research findings in the area.