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).

Detecting and Mitigating the Dissemination of Fake News: Challenges and Future Research Opportunities

Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. Researchers from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection, and mitigation of fake news;however, it remains challenging to detect and prevent the dissemination of fake news in practice. In addition, we emphasize the importance of designing artificial intelligence (AI)-powered systems that are capable of providing detailed, yet user-friendly, explanations of the classification / detection of fake news. Hence, in this article, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present a potential future research agenda, especially incorporating AI explainable fake news credibility system.

The Security of Blockchain-Based Medical Systems: Research Challenges and Opportunities

The current COVID-19 pandemic has, perhaps, expedited the move to electronic medical systems (e.g., telemedicine). However, in the digitalization of healthcare services, we have to ensure the security and privacy of (sensitive) healthcare data, often stored locally in the hospital's server or remotely within a trusted cloud server. There have been many attempts to design blockchain-based approaches to support security and privacy in medical systems, and this is the focus of this article where we systematically review the existing literature on blockchain-based medical systems. We then categorize the existing security solutions into three categories, namely, 1) decentralized authentication, 2) access control, and 3) audit, and discuss the privacy protection technologies in blockchain-based healthcare systems. Based on our analysis, we identify a number of challenges, including performance limitations and inflexible audit, as well as future research opportunities (e.g., the need for lightweight security schemes for blockchain-based medical systems).

Investigating formation of collective identity driving the black lives matter movement during COVID-19

Focusing on the recent Black Lives Matter (BLM) movement associated with the death of George Floyd during the COVID-19 pandemic, we seek to identify the shared collective identity of online and offline participants. Specifically, we collect hashtags associated with the movement and use sentiment analysis to investigate the individual emotions that underpin their involvement during the COVID-19 pandemic. The link between online activism and offline protests is modelled in our study. Users' beliefs serve as factors that direct actions (emotions) resulting in significant outcomes (protests) and are moderated by collective identity of the people participating in the protests. We use natural language processing (NLP) to test for the presence of the identified factors in our tweet corpus of 8 weeks of data (from 05/2020 to 10/2020) from twitter that involves discussions around the #BLM. © AMCIS 2021.

Artificial Intelligence Technologies for COVID-19-Like Epidemics: Methods and Challenges

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress. © 1986-2012 IEEE.

Fighting COVID-19 and Future Pandemics With the Internet of Things: Security and Privacy Perspectives

The speed and pace of the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2;also referred to as novel Coronavirus 2019 and COVID-19) have resulted in a global pandemic, with significant health, financial, political, and other implications. There have been various attempts to manage COVID-19 and other pandemics using technologies such as Internet of Things (IoT) and 5G/6G communications. However, we also need to ensure that IoT devices used to facilitate COVID-19 monitoring and treatment (e.g., medical IoT devices) are secured, as the compromise of such devices can have significant consequences (e.g., life-threatening risks to COVID-19 patients). Hence, in this paper we comprehensively survey existing IoT-related solutions, potential security and privacy risks and their requirements. For example, we classify existing security and privacy solutions into five categories, namely: authentication and access control solutions, key management and cryptography solutions, blockchain-based solutions, intrusion detection systems, and privacy-preserving solutions. In each category, we identify the associated challenges. We also identify a number of recommendations to inform future research.

Propagation of Fake News on Social Media: Challenges and Opportunities

Fake news, particularly with the speed and reach of unverified/false information dissemination, is a troubling trend with potential political and societal consequences, as evidenced in the 2016 United States presidential election, the ongoing COVID-19 pandemic, and the ongoing protests. To mitigate such threats, a broad range of approaches have been designed to detect and mitigate online fake news. In this paper, we systematically review existing fake news mitigation and detection approaches, and identify a number of challenges and potential research opportunities (e.g., the importance of a data sharing platform that can also be used to facilitate machine/deep learning). We hope that the findings reported in this paper will motivate further research in this area. © 2020, Springer Nature Switzerland AG.

Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems

Wearable body area network is a key component of the modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for a secure collection of medical data, there is also a need to process data in real-time. In this article, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications. Specifically, medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example, allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e., chosen-plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme. © 2005-2012 IEEE.

Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images

The real-time sharing and retrieval of medical data, such as medical imaging data, via cloud systems can facilitate timely medical/disease diagnosis, for example during pandemics (e.g., COVID-19). While encryption can be used to ensure that patients’ private and medical information are not accessible by unauthorised individuals, it is challenging for cloud servers to search for and locate encrypted medical images (e.g. those relating to similar medical conditions). In this paper, we propose a novel and practical classification and retrieval method to search for and locate relevant cases over encrypted images. Specifically, we construct a privacy-preserving Convolutional Neural Network (CNN) framework that allows the classification and searching of secure, content-based, large-scale encrypted images (including large-size medical images) with homomorphic encryption. We analyze the security of our proposed method to ensure that no sensitive information from the encrypted images is leaked. Using four real-world datasets (i.e., chest X-Ray images, retinal OCT images, blood cell images, and Caltech101 image set), we evaluate and demonstrate the utility of our privacy-preserving method for searching images performed as well as CNN-based classification and searching of original images. This is an important step towards practical automated clinical diagnoses. © 2020 Elsevier Ltd

Differentially private publication of streaming trajectory data

User-generated trajectories (e.g. during traveling) can be leveraged to offer value-added services (e.g. smart city policy formulation), but there are also privacy implications. For example, information about the routes or destinations obtained from such published trajectories can be used to profile and identify users, including during contact tracing in pandemics (e.g., COVID-19) or the monitoring of demonstrations (e.g., surveillance). However, existing trajectory publishing algorithms generally rely on batch processing platforms, and rarely pay attention to real-time privacy protection processing in streaming scenarios. Therefore, we propose a stream processing framework containing two modules for spatio-temporal data. This framework is designed to achieve high data utility, while effectively ensuring the preservation of privacy in the published results. The first module is TSP, which concurrently receives real-time queries from individuals and releases new sanitizing trajectories. The second module is VCR comprising three algorithms based on differential privacy to facilitate the publication of the distribution of position statistics. Our experiments on real-world datasets demonstrate that our framework can effectively guarantee privacy with high data utility, when the appropriate parameter configuration is chosen. In addition, compared with the baseline algorithm Ht-publication, our group-based algorithm AGn-publication achieves better data accuracy in terms of visitor counts at the same level of privacy protection. © 2020 Elsevier Inc.