D2Pt: Privacy-Aware Multiparty Data Publication

Abstract

Today, publication of medical data faces high legal barriers. On the one hand, publishing medical data is important for medical research. On the other hand, it is neccessary to protect peoples' privacy by ensuring that the relationship between individuals and their related medical data remains unknown to third parties. Various data anonymization techniques remove as little identifying information as possible to maintain a high data utility while satisfying the strict demands of privacy laws. Current research in this area proposes a multitude of concepts for data anonymization. The concept of k-anonymity allows data publication by hiding identifying information without losing its semantics. Based on k-anonymity, the concept of t-closeness incorporates semantic relationships between personal data values, therefore increasing the strength of the anonymization. However, these concepts are restricted to a centralized data source. In this paper, we extend existing data privacy mechanisms to enable joint data publication among multiple participating institutions. In particular, we adapt the concept of t-closeness for distributed data anonymization. We introduce Distributed two-Party t-closeness (D2Pt), a protocol that utilizes cryptographic algorithms to avoid a central component when anonymizing data adhering the t-closeness property. That is, without a trusted third party, we achieve a data privacy based on the notion of t-closeness.