An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model <inline-formula><tex-math notation="LaTeX">$l_{c}, l_{s}$</tex-math></inline-formula>-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (<inline-formula><tex-math notation="LaTeX">$V_{c0}$</tex-math></inline-formula>) and a Vulnerable sensitive attribute attack (<inline-formula><tex-math notation="LaTeX">$V_{sa}$</tex-math></inline-formula>) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.