Bridging unlinkability and data utility: Privacy preserving data publication schemes for healthcare informatics

Publishing patient data without revealing their sensitive information is one of the challenging research issues in the healthcare sector. Patient records contain useful information that is often released to healthcare industries and government institutions to support medical and census research. There are several existing privacy models in protecting healthcare data privacy, which are mainly built upon the anonymity of patients. In this paper, we incorporate unlinkability in the context of healthcare data publication, where two new privacy notions namely identity unlinkability and attribute unlinkability are introduced. We design two schemes using the proposed models to address identity disclosure and attribute disclosure problems in publishing healthcare data. Experimental results on real and synthetic datasets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.

Automated summary

Publishing patient data without revealing sensitive information is a challenging research issue in the healthcare sector. This paper introduces two new privacy notions, namely identity unlinkability and attribute unlinkability, and designs schemes to address identity and attribute disclosure problems while preserving data utility. Experimental results demonstrate the effectiveness of our schemes in achieving both data utility preservation and privacy protection simultaneously.