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Article

θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records

1
National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
3
Cybernetica AS Estonia, Tallinn 13412, Estonia
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Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
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Department of Computer Science, University of Peshawar, Peshawar 25120, Pakistan
6
Warwick Manufacturing Group, The University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 716; https://doi.org/10.3390/electronics9050716
Received: 28 March 2020 / Revised: 19 April 2020 / Accepted: 20 April 2020 / Published: 26 April 2020
(This article belongs to the Special Issue Cyber Security for Internet of Things)
The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%). View Full-Text
Keywords: Internet of Things; big data; electronic health records; k-anonymity; privacy; security Internet of Things; big data; electronic health records; k-anonymity; privacy; security
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MDPI and ACS Style

Khan, R.; Tao, X.; Anjum, A.; Kanwal, T.; Malik, S.u.R.; Khan, A.; Rehman, W.u.; Maple, C. θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records. Electronics 2020, 9, 716. https://doi.org/10.3390/electronics9050716

AMA Style

Khan R, Tao X, Anjum A, Kanwal T, Malik SuR, Khan A, Rehman Wu, Maple C. θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records. Electronics. 2020; 9(5):716. https://doi.org/10.3390/electronics9050716

Chicago/Turabian Style

Khan, Razaullah, Xiaofeng Tao, Adeel Anjum, Tehsin Kanwal, Saif u.R. Malik, Abid Khan, Waheed u. Rehman, and Carsten Maple. 2020. "θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records" Electronics 9, no. 5: 716. https://doi.org/10.3390/electronics9050716

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