Capturing the effects of attribute based correlation on privacy in micro-databases

Abstract

In the modern data driven era, it is a very common practice for individuals to provide their personalized data in multiple databases. However, the existence of correlated information in between these databases is a common source of privacy risk for the database users. In our study, we investigate such scenarios for attribute based linking attacks. These attacks refer to the common strategy by which an adversary can breach the privacy of the database respondents via exploiting the correlated information among the database attributes. In our work, we have proposed an information theoretic framework through which the achievable privacy levels following an adversarial linking attack are quantified. Our developed model also incorporates various aspects associated with micro-databases such as sanitization mechanism and auxiliary side information, thereby providing a more holistic structure to our theoretical framework. A comparative analysis of the various cases associated with our model theoretically confirms the notion that a sanitization mechanism facilitates in preserving the original privacy levels of the users. Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.