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

Abstract

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%).