A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes. Issue 105 (June 2021)

• Record Type: Journal Article
• Authors: Kanwal, Tehsin; Anjum, Adeel; Malik, Saif U.R.; Sajjad, Haider; Khan, Abid; Manzoor, Umar; Asheralieva, Alia
• Is Part Of: Computers & security. Issue 105(2021)
• Publication Date: 2021-06
• Subjects: Electronic Health Record -- Identity Disclosure -- Sensitive Attribute Disclosure -- Balanced p+ sensitive k anonymity model -- Formal Verification -- Privacy-Preserving -- Multiple Sensitive Attributes (MSAs); Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2021.102224
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: Privacy preserving data publishing of electronic health record (EHRs) for 1 to M datasets with multiple sensitive attributes (MSAs) is an interesting and challenging issue. There is always a trade-off between privacy and utility in data publishing. Most of the privacy-preserving models shows critical privacy disclosure issues and, hence, they are not robust in practical datasets. The k -anonymity model is a broadly used privacy model to analyze privacy disclosures, however, this model is only useful against identity disclosure. To address the limitations of k -anonymity, a group of privacy model extensions have been proposed in past years. It includes a p -sensitive k -anonymity model, a p+ -sensitive k -anonymity model, and a balanced p+ -sensitive k -anonymity model. However these privacy-preserving models are not sufficient to preserve the privacy of end-users in practical datasets. In this paper we have formalize the behavior of an adversary which perform identity and attribute disclosures on balanced p + -sensitive k -anonymity model with the help of adversarial scenarios. Since balanced p + -sensitive k -anonymity model is not sufficient for 1 to M with MSAs datasets privacy preservation. We propose an extended privacy model called " 1: M MSA-(p, l)-diversity " for 1: M dataset with MSAs. We then perform formal modeling and verification of the proposed model using High-Level Petri Nets (HLPN) to confirm privacy attacks invalidation. Experimental results show that our proposed " 1: M MSA-(p, l)-diversity model" is efficient and provide enhanced data utility of published data.