Factors Affecting Continuance Intention in Non-Face-to-Face Telemedicine Services: Trust Typology and Privacy Concern Perspectives.

As the COVID-19 pandemic progressed, the resulting demand for telemedicine services increased. This research empirically examines the role of trust, privacy concerns, and perceived usefulness in customer confirmation, satisfaction, and continuing intention in telemedicine. A typology of trust was employed to classify trust into three dimensions and explore the mediating role of the three dimensions of trust in the relationship between satisfaction, perceived usefulness, and continued intention. We also examined the moderating role of personal privacy concerns in the relationship between trust and continued intention. For this study, we developed a structural equation model based on expectation confirmation theory and analyzed 465 questionnaires from Chinese online users. The expectancy confirmation theory (ECT) was reaffirmed by empirical evidence. The results showed that the relationship between perceived usefulness and satisfaction with continued intention is moderated by the three dimensions of trust. Privacy concerns can negatively moderate the relationship between structural assurance-based trust and continued intention. This study also identified potential threats to telehealth market growth alongside new insights.

Study on the Discovery of Personal Information on the Network of People Diagnosed with COVID-19

The arbitrary disclosure of information of people diagnosed with COVID-19on the network will adversely affect personal privacy and even violate the privacy rights of individuals. Through the method of literature analysis and case analysis, the information of the confirmed patients of COVID-19 is studied on the network disclosure. The study found that information disclosure can be divided into disclosable information and non-disclosure information, and make different ways of dealing with sensitive information, sensitive information must be handled with care, personal information processing must take into account the balance between personal interests and public interests. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

A Critique of EU Digital COVID-19 Certificates: Do Vaccine Passports Endanger Privacy?

Do COVID-19 vaccine passports come at a fundamental cost for personal privacy? Reviewing proposed COVID-19 credentials from a security and privacy standpoint raises concerns that make deploying COVID-19 digital certificates difficult at best. A closer look into the privacy of the EU Digital COVID-19 certificate presents a fundamental contradiction between two essential security properties: unforgeability and privacy. A substantial reconsideration of the very concept of vaccine passports may be needed to preserve fundamental privacy rights. © 2022 Owner/Author.

AMDetector: Detecting Large-Scale and Novel Android Malware Traffic with Meta-learning

In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

IoT Based System for Detecting the Number of People and their Distribution in Classroom

This paper proposes a smart system for detecting the number of people in the classroom and their distribution over the available seats. The system is based on Arduino nodes used as the Internet of Things (IoT) modules and Raspberry PI as the central unit for data collection, evaluation, and storage. The system's primary purpose is to evaluate the number of people in the classroom because of the COVID-19 restrictions and automatically check the distance between sitting people. During the system design, we put personal privacy in the first place, and therefore we do not use any cameras. © 2022 IEEE.

Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea.

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.