Tokens Shuffling Approach for Privacy, Security, and Reliability in IoHT under a Pandemic

Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data's privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system's performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services. © 2022 by the authors.

A Smart Framework to Analyze Hotel Services after COVID-19

In the last decade, many websites have allowed customers to provide reviews about their experiences in dealing with different hotels like describing their opinions about different sides like cleanliness, food, employees, and other services. However, after COVID-19, new concerns have emerged linked to the health precautionary and preventive processes. At the same time, the amount of generated data (Textual Data) has become very huge (Big Data), and it needs to auto classifier for processing it where it is impossible to review manually. So, this research proposed a smart method to detect useful information from text by extracting the interesting services from comments automatically. The method depended on machine learning (ML) and compared between five different classification models (Spacy, Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Passive Aggressive, and AdaBoost) to determine which one provides the best results and accuracy. According to the experiment on a real dataset containing more than 1000 records, the NB was the best accurate with 98%, while the Spacy was less and that related to the small size of training data. © 2022 Bharati Vidyapeeth, New Delhi.