A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes

Kanwal，Tehsin1; Anjum，Adeel1,5; Malik，Saif U.R.2; Sajjad，Haider1; Khan，Abid3; Manzoor，Umar4; Asheralieva，Alia5

2021-06-01

10.1016/j.cose.2021.102224

COMPUTERS & SECURITY   影响因子和分区

0167-4048

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.

Balanced p+ sensitive k anonymity model Electronic Health Record Formal Verification Identity Disclosure Multiple Sensitive Attributes (MSAs) Privacy-Preserving Sensitive Attribute Disclosure

SCI ; EI

英语

通讯

WOS:000643675100007

20211110062158

Data privacy ; Economic and social effects ; Formal verification ; Petri nets

Computer Applications:723.5 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Social Sciences:971

COMPUTER SCIENCE

2-s2.0-85102399890

Scopus

1.Department of Computer Sciences,Comsats University Islamabad,Pakistan
2.Senior Researcher,Cybernetica,Estonia
3.Department of Computer Science,Aberystwyth University,Aberystwyth,SY23 3DB,United Kingdom
4.Department of Computing,University of Hull,Hull,United Kingdom
5.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,Xueyuan Avenue, Nanshan District,China

Kanwal，Tehsin,Anjum，Adeel,Malik，Saif U.R.,et al. A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes[J]. COMPUTERS & SECURITY,2021,105.

Kanwal，Tehsin.,Anjum，Adeel.,Malik，Saif U.R..,Sajjad，Haider.,Khan，Abid.,...&Asheralieva，Alia.(2021).A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes.COMPUTERS & SECURITY,105.

Kanwal，Tehsin,et al."A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes".COMPUTERS & SECURITY 105(2021).