The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient’s data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients’ data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

With the advent of modern information systems, sharing Electronic Health Records (EHRs) with different organizations for better medical treatment, and analysis is beneficial for both academic as well as for business development. However, an individual’s personal privacy is a big concern because of the trust issue across organizations. At the same time, the utility of the shared data that is required for its favorable use is also important. Studies show that plenty of conventional work is available where an individual has only one record in a dataset (1:1 dataset), which is not the case in many applications. In a more realistic form, an individual may have more than one record in a dataset (1:M). In this article, we highlight the high utility loss and inapplicability for the 1:M dataset of the θ-Sensitive k-Anonymity privacy model. The high utility loss and low data privacy of (p, l)-angelization, and (k, l)-diversity for the 1:M dataset. As a mitigation solution, we propose an improved (θ∗, k)-utility algorithm to preserve enhanced privacy and utility of the anonymized 1:M dataset. Experiments on the real-world dataset reveal that the proposed approach outperforms its counterpart, in terms of utility and privacy for the 1:M dataset.