A Novel Framework for Preventing Inference Attacks in Collaborative Data Publishing?

Disclosure of sensitive data is the problem in collaborative data publishing. Collaborative data publishing involves multiple parties where data privacy is very important. There are number of threats to the privacy of data. For instance, there is possibility for insider attacks to obtain identity of real world objects. The data sources provided by multiple parties for collaborative publishing are to be protected from disclosure attacks. Moreover, the sensitive details are to be protected from being disclosed. To overcome this problem, many anonymization techniques came into existence. M-Privacy is one such good algorithm proposed by Goryczka et al. that provides dependable security to collaborative data publishing. In this paper, we propose a framework that that resolves the problem of identity disclosure in the context of collaborative publishing of data. We built a prototype application that demonstrates the proof of concept. Our empirical results are encouraging.