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%).

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Electronic health records (EHRs) are increasingly employed to maintain, store and share varied types of patient data. The data can also be utilized for various research purposes, such as clinical trials or epidemic control strategies. With the increasing cost and scarcity of healthcare services, healthcare organizations feel at ease in outsourcing these services to cloud-based EHRs. That serves as pay-as-you-go (PAYG) "e-health cloud" models to aid the healthcare organizations handling with existing and imminent demands yet restricting their costs. Technologies can host some risks; hence the privacy of information in these systems is of utmost importance. Regardless of its increased effectiveness and growing eagerness in its adoption, not much care is being employed to the privacy issues that might arise. Privacy preservation need to be reviewed about the changing privacy rules and legislations regarding sensitive personal data. Our work aims at answering three major questions: firstly, how privacy models and privacy techniques correlate with each other, secondly, how we can fix the privacy-utility-trade off by using different combinations of privacy models and privacy techniques and lastly, what are the most relevant privacy techniques that can be adapted to achieve privacy of EHR on cloud.

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.