# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 4 Discussion
### Privacy and Security Issues  
Due to the urgency of the situation to manage and develop solutions for the COVID-19 pandemic, information sharing in large volumes between international and national organizations (e.g., people with the virus (also known as electronic health records) and treatment data, contact tracing data, multimedia data produced by digital applications such as Zoom, Meet, Webex, Teams, etc.) has become inevitable. Furthermore, information sharing is an important building block for the realization of Big Data, and at the same time, it can lead to infringements on individual privacy. Due to the COVID-19 pandemic, different kinds of privacy and security issues have been reported in media around the world due to massive data collection and processing to curb the spread of COVID-19. Hence, the need to preserve people's privacy has become more urgent than ever. Due to privacy issues at a large scale, we may require more robust organizational, constitutional, and technical measures to address the security and privacy issues of digital applications in the COVID-19 era. Furthermore, heavy reliance on digital solutions, such as IoE and IoT sensors, Internet, SN, 5G networks, and HPC-based computing powers, will make the problem even worse. The ongoing pandemic has highlighted that existing privacy and security mechanisms are not sufficient to address the privacy and security implications emerging from the ongoing pandemic. They require a brand-new start to adhere to more stern data protection laws and regulations (e.g., HIPAA, IPA, GDPR, etc.) around the world to comfort people regarding their privacy requirements. We highlight the privacy and security issues of emerging Big Data applications and other digital technologies that are extensively used during the era of the COVID-19 pandemic in Figure 6. Recently, numerous pertinent solutions focusing on privacy preservation have been proposed [16; 17; 18; 19; 20; 21]. However, general public trust in digital solutions use remained very low, mainly due to privacy issues. Therefore, the privacy and security issues presented in Figure 6 require a brand-new start to develop more practical solutions to serve the community in effective ways. Architecture of Prototype System to Demonstrate the Effectiveness of AI and HPC Techniques in COVID Context  
From the start of this unprecedented pandemic, a number of digital solutions have been proposed for different utilities such as digital contact tracing [22], IoT-based generic models for other infectious disease and COVID-19 suspects tracing [23], predictions of COVID-19 malignant progression [24], medical AI systems [25], expert systems for clinical guidelines [26], and real-time data based COVID-19 screening using wearable sensors data [27], to name a few. After a detailed synthesis of these systems, we found that all existing solutions were proposed to address one aspect of the COVID-19 pandemic. To address this deficiency, we designed an architecture of the prototype system to demonstrate the effectiveness of the AI and HPC techniques in the COVID-19 context and the need for such a system. The need for an AI- and HPC-powered system is demonstrated in Figure 7a, and the proposed prototype system architecture is shown in Figure 7b. The proposed prototype has three main modules that can be enhanced more in the future. The main modules, along with their brief description, are presented below. * Data collection: In this module, data are collected from relevant individuals and authentic sources such as healthcare departments and agencies. We identify three main sources of data that can contribute effectively to this pandemic, such as real-environment data (including social distancing, mask information, nature of contact with other people, spatiotemporal-based activities data, social circle information, etc. ), authentic healthcare departments data (including previous disease history, travel information, ambient data, etc. ), and IoT and SN data (including people behaviors).