Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

Time-frequency volatility connectedness between fossil energy and agricultural commodities: Comparing the COVID-19 pandemic with the Russia-Ukraine conflict

This paper uses the TVP-VAR frequency connectedness approach to compare the volatility connectedness induced by the COVID-19 pandemic and the Russia-Ukraine conflict. Both shocks induce increased connectedness, but the pandemic shock is stronger. High-frequency and medium-frequency connectedness dominate during the early stage of the pandemic, while low-frequency connectedness dominates during the conflict. Moreover, fossil energy is the risk transmitter in the early phase of the pandemic, while agricultural commodities become the transmitter during the conflict. We should take precautions against risks contagion from fossil energy and agricultural commodities to the post-conflict economy, and prevent inflation and economic slowdown. © 2023 The Author(s)

Privacy-Preserving Fast Authentication and Key Agreement for E-health Systems in IoT, based on Three-Factor Authentication

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9\% in comparison to three other related schemes). IEEE

Evaluation of an Online 360 degrees Virtual Reality World Heritage Site During COVID-19

This study developed a framework to evaluate, in the context of COVID-19, the performance of an OVRWCHT (online 360 degrees virtual reality world cultural heritage tourism) system created by the authors for the purpose of heritage interpretation and presentation. The research framework was based on the seven main principles of the ICOMOS Charter for the Interpretation and Presentation of Cultural Heritage Sites, and evaluation criteria were established for each. This framework was used to evaluate an OVRWCHT for the Hailongtun Tusi World Heritage Site in Guizhou Province, China. Data were mainly based on 1,062 questionnaires and analyses of the developed system. The findings indicated that, whether in terms of user experience or the interpretation of the UNESCO criterion "outstanding universal value," Stakeholders agreed that OVRWCHT has played a positive role in heritage interpretation. Yet, more data support is needed to improve both technology and theory - especially the transferability of OVRWCHT to countries other than China. Based on the findings, it is suggested that the International Council on Monuments and Sites should continue to issue charters on how emerging technologies can support heritage site interpretation and presentation.

COVIZ: Visualization of Effects of COVID-19 on New York City Through Socially Impactful Virtual Reality

This work is the product of a collaboration between students studying computer science and social work to visualize the impacts and effects of COVID-19 in New York City in a virtual environment (VE). As a proof of concept, the team chose two datasets from NYC Open Data;COVID-19 infection cases and rates per zip code and vehicular traffic rates within the five boroughs of New York City. To foster unexplored insights into the relationship between these data, we developed a virtual reality application that provides a stronger sense of embodiment and ownership of urban visualization analysis when manipulating 3D virtual maps for comparison in a VE.

A Study on College Graduates' Employment Problem in the Context of Big Data Based on the Event of COVID-19

Affected by the outbreak of COVID-19, the employment pressure of college graduates is increasing. The employment problem of graduates is a livelihood issue which is highly valued by social community. The arrival of big data era has a profound impact on the employment situation analysis, policy formulation, employment service and guidance, and college graduates' career development. Therefore, to explore the impact of the epidemic on the employment situation of graduates, this paper takes college graduates of the University of Electronic Science and Technology of China as the survey object, and uses questionnaires to investigate the employment status of college graduates under the background of the COVID-19. Starting from the three dimensions of the government, universities, and individual college graduates, to analyze the employment status and problems of graduates, and to explore ways to use big data to promote employment of college graduates under the influence of the COVID-19. This is of great significance for alleviating employment pressure and improving the quality of employment. © 2021 ACM.

Transformer-Based Language Model Fine-Tuning Methods for COVID-19 Fake News Detection

With the pandemic of COVID-19, relevant fake news is spreading all over the sky throughout the social media. Believing in them without discrimination can cause great trouble to people’s life. However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge. While the model trained on corresponding corpora is also mediocre for insufficient learning. In this paper, we propose a novel transformer-based language model fine-tuning approach for these fake news detection. First, the token vocabulary of individual model is expanded for the actual semantics of professional phrases. Second, we adapt the heated-up softmax loss to distinguish the hard-mining samples, which are common for fake news because of the disambiguation of short text. Then, we involve adversarial training to improve the model’s robustness. Last, the predicted features extracted by universal language model RoBERTa and domain-specific model CT-BERT are fused by one multiple layer perception to integrate fine-grained and high-level specific representations. Quantitative experimental results evaluated on existing COVID-19 fake news dataset show its superior performances compared to the state-of-the-art methods among various evaluation metrics. Furthermore, the best weighted average F1 score achieves 99.02%. © 2021, Springer Nature Switzerland AG.

SIP: An Efficient and Secure Information Propagation Scheme in E-health Networks

Electronic healthcare (e-health) networks are increasingly popular, particular during pandemics such as COVID-19. This reinforces the importance of ensuring security and privacy for data-in-transit. One such solution is steganography-based schemes that utilize biological signals (e.g., ECG) as cover signals to preserve the privacy of patient personal information without affecting the diagnostic features. There are various limitations in existing steganography-based schemes, and in this study we present an effective privacy protection scheme leveraging both multidimensional steganography and shared keys. To enhance security and accelerate signal processing in our design, the Fast Walsh-Hadamard transform (FWHT) is employed to decompose ECG signals into a set of coefficients, of which the less-significant coefficients are used to construct the multidimensional space. The negotiated shared keys facilitate the embedding of encrypted data in the constructed space. We then evaluate the proposed scheme using different categories of ECG signals in the MIT-BIH database. It is observed that the signal distortion is minimal (i.e., less than 1\%), even if the embedded data reaches the maximum embedding capacity. The security analysis also demonstrates that unauthorized retrieval of hidden information is not practical, within a short period of time. IEEE

Analysis on diagnosis and treatment scheme of traditional Chinese medicine in treatment of COVID-19 in Chinese provinces and regions

Since December 2019, a novel coronavirus-infected pneumonia was first detected in Wuhan, such cases had been subsequently discovered in other cities. The disease caused by the novel coronavirus was officially named COVID -19 (coronavirus disease 2019) by the world health organization. National Health Commission of China and other provinces and cities have successively performed syndrome differentiation of COVID-19 and provided corresponding Chinese medicine treatment programs. In this epidemic, the disease is a “dampness toxin”. The best principle for treatment is early detection and early treatment. Both Chinese and Western medicine have their own advantages. The advantages could be complementary and could not be replaced each other. Therefore, we collected the Chinese medicine treatment programs for the treatment of COVID-19 comprehensively, conducted a systematic analysis, and especially analyzed the pharmacological basis of traditional Chinese medicine for the treatment of COVID-19, which provided a basis for the rationality of Chinese medicine prescription for the treatment of COVID-19, and provided a reference of updating the diagnosis and treatment plan for provinces and cities.