BEsafe - Validating URLs and Domains with the aid of Indicator of Compromise

The advancement of information technology has stimulated the conversion of physical interactions to online activities, especially during the Covid-19 pandemic. Thus, users' awareness and cyber hygiene need to be emphasized when they are involved in the cyber world. A browser extension named 'BEsafe' is developed to validate the websites and promote a safe browsing environment. It prevents users from falling prey to network-based attacks and raises their security awareness. To ensure users' privacy, the permissions needed for BEsafe are listed on the permission tab. Moreover, BEsafe will not be working on Incognito mode by default to promise that the private mode leaves no tracks. However, the user can still enable the extension to be functioning on Incognito mode by navigating to the Extension Details and turning on the relevant toggle. © 2023 IEEE.

COVID-19 Contact-Tracing Technology, Users' Privacy and Security

Nowadays, the entire world is struggling to adapt and survive the SARS-CoV-2/COVID-19 pandemic, the new mutations in the Coronavirus disease is causing damage and disruption across the world. Taking preventive measures to control the spreading of the virus, including lockdowns, curfews, social distancing, masks, vaccination are not enough to stop the virus. Many countries have sought to support their contact tracers with the use of digital contact tracing apps to manage and control the spread of the virus. Using the new technologies to adapt the prevention measures furthermore enhancing the existing ones, will definitely be more efficient. There are many contact tracing apps that have already been launched and used since 2020. There has been a lot of speculations about the confidentiality and security aspects of these apps and their possible violation of data protection principles. In this paper we propose a system of contact tracing, we explain how this system treats sensible information to preserve the user's identity and protect their personal information. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analyzing Learners' Privacy in MOOC and Online Learning Platform

COVID-19 has affected human life since its advent. And to counteract its spread, humankind adopts social distancing, which encourages remote working for employees, and online learning for students. Many universities and schools quickly adopted e-learning solutions without much consideration of security, while it is important to consider users' privacy. Unfortunately, digital learning spaces face security vulnerabilities, risks and threats and are not spared from cyber-attacks. To ensure the security and privacy of e-learning solutions used by universities and schools, we analyzed how MOOCs and Organizations offering online courses long before COVID-19 deal with their users' privacy and personal data. In this study, we considered some popular platforms from The United States (Coursera, EdX, Udemy), Europe and the United Kingdom (FutureLearn, FUN MOOC, EduOpen), and Asia (XuetangX, SWAYAM, and K-MOOC). We discussed the personal data collected by these platforms, the purposes for which these data are collected, the different legislation for processing and storing data, and how the platforms ensure user privacy. © 2022 IEEE.

The New Progress and Methods of Privacy Protection on Medical and Health Big Data*

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.

"I just want to play games with friends and it asked me for all of my information": Trading privacy for connection during the COVID-19 pandemic

We conducted 22 semi-structured interviews with participants in the early stages of the COVID-19 pandemic when restrictions were in effect, to learn about social media users' privacy behaviors and what influenced changes in behavior since the beginning of the pandemic. We found that participants felt pressured to stay "relevant"online, which led to increased consumption and sharing of content, as well as increased re-posting of older content. Participants also noted increased disclosure of negative emotional states and that they were expected to publicly display their stance in regards to social movements. Participants felt increasingly reliant on social media as a means of connection which led them to download and install additional social apps despite privacy concerns. Each of these activities has potential privacy implications in terms of explicit data sharing and in terms of increased sources of information for online behavioral tracking and profiling. © 2022 ACM.

RESEARCH ON THE WILLINGNESS OF USERS' PRIVACY CONCERNS TO USE EPIDEMIC PREVENTION TECHNOLOGY UNDER THE BACKGROUND OF THE NEW CORONAVIRUS EPIDEMIC

Nowadays, governments worldwide use artificial intelligence (AI), big data, cloud computing, and other technologies to control the spread of the epidemic. These measures significantly improve the efficiency of virus tracking. Nevertheless, such digital defences have also raised concerns about privacy leaks. Privacy concerns and use intention have been studied in several areas in the existing literature, but few have been explored in-depth based on epidemic prevention. Therefore, this paper focuses on the background of the novel coronavirus epidemic and constructs a structural equation model based on the theory of privacy concern and technology acceptance model. The research studies the influence of privacy concerns, perceived risk, and other factors on users’ willingness to use new technologies. Based on 132 samples, the results show that privacy concerns significantly impact perceived risk. Perceived trust has significant positive impacts on self-disclosure intention. This study discusses individual self-disclosure intention in the field of public security from multiple perspectives. The research results extend the relevant theories on adopting and using emerging technologies. This study provides ideas on how to alleviate residents' privacy concerns in practice and helps government departments to carry out better prevention work. © 2022 CURRAN-CONFERENCE. All rights reserved.

Mobile Applications for Privacy-Preserving Digital Contact Tracing

Mobile applications for triggering Covid-19 exposure notifications without sacrificing the users' privacy are a promising tool for complementing manual contact tracing that is a resource-demanding and labor-intensive task when the infections grow rapidly. This advanced seminar presents the fundamental concepts behind the realization of large-scale Mobile Contact Tracing Apps (MCTA). We provide an overview of this emerging field, while focusing on Bluetooth-based privacy-preserving solutions. We tackle the topic from multiple perspectives: background, state-of-the-art technologies, protocols, real-life implementations, performance indicators, security and privacy aspects, as well as future directions. The seminar presents the big picture, so that the target audience can further expand their knowledge by studying the material and following the references. Our presentation will be delivered through the lens of 2 country-wide MCTA, namely the Corona-Warn-App (CWA) and the CovTracer-Exposure Notification (CovTracer-EN) app deployed in Germany and Cyprus, respectively. © 2022 IEEE.

Combining Artificial Intelligence and Edge Computing to Reshape Distance Education (Case Study: K-12 Learners)

Nowadays, the use of distance learning is increasing, especially with the recent Covid-19 pandemic. To improve e-learning and maximise its effectiveness, artificial intelligence (AI) is used to analyse learning data stored in central repositories (e.g. in cloud). However, this approach provides time-lagged feedback and can lead to a violation of user privacy. To overcome these challenges, a new distributed computing paradigm is emerging, known as Edge Computing (EC), which brings computing and data storage closer to where they are required. Combined with AI capabilities, it can reshape the online education by providing real-time assessments of learners to improve their performance while preserving their privacy. Such approach is leading to the convergence of EC and AI and promoting AI at the Edge. However, the main challenge is to maintain the quality of data analysis on devices with limited memory capacity, while preserving user data locally. In this paper, we propose an Edge-AI based approach for distance education that provides a generic operating architecture for an AI unit at the edge and a federated machine learning model to predict at real-time student failure. A real-world scenario of K-12 learners adopting 100% online education is presented to support the proposed approach. © 2022, Springer Nature Switzerland AG.

A Machine Learning-Based Privacy-Preserving Model for COVID-19 Patient using Differential Privacy

Machine learning models are often trained on the dataset, which contains sensitive information. Private Aggregation of Teacher Ensembles is a helpful framework that can be used along with Differential privacy to preserve users' privacy. The teacher models are trained on a disjoint dataset, and the aggregated knowledge of the teacher model is then used to train the student model. The proposed method used the teacher models technique to detect whether a person's chest CT-Scan is affected by COVID-19 or not while preserving the user's privacy and compared the proposed privacy-preserving model with a standard CNN model to compare their accuracy. © 2021 IEEE.

Does a Face Mask Protect My Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images

Contactless and efficient systems are implemented rapidly to advocate preventive methods in the fight against the COVID-19 pandemic. Despite the positive benefits of such systems, there is potential for exploitation by invading user privacy. In this work, we analyse the privacy invasiveness of face biometric systems by predicting privacy-sensitive soft-biometrics using masked face images. We train and apply a CNN based on the ResNet-50 architecture with 20,003 synthetic masked images and measure the privacy invasiveness. Despite the popular belief of the privacy benefits of wearing a mask among people, we show that there is no significant difference to privacy invasiveness when a mask is worn. In our experiments we were able to accurately predict sex (94.7%), race (83.1%) and age (MAE 6.21 and RMSE 8.33) from masked face images. Our proposed approach can serve as a baseline utility to evaluate the privacy-invasiveness of artificial intelligence systems that make use of privacy-sensitive information. We open-source all contributions for reproducibility and broader use by the research community. © 2022, Springer Nature Switzerland AG.

MLCT: A multi-level contact tracing scheme with strong privacy

With the outbreak of Covid-19, both people's health and the world economy are facing great challenges. Contact tracing scheme based on Bluetooth of smartphones has been regarded as a viable way to mitigate the spread of Covid-19. The existing schemes mainly belong to the centralized or the decentralized structure, both of which have their own limitations. It is infeasible for the existing schemes to balance the different demands of governments and users for user privacy and tracing efficiency at different periods of the epidemic. In this paper, we propose a hybrid contact tracing scheme named MLCT (multi-level contact tracing scheme) which is mainly based on short group signature. MLCT provides multiple privacy levels by applying anonymous credential technology and secret sharing technology to desensitize user identity privacy and encounter privacy. Comparing to the previous schemes, MLCT fully considers the different demands of the government, patients, and close contacts for user privacy and tracing efficiency in the different stages of Covid-19. The experimental results show viability in terms of the required resource from both server and mobile phone perspectives. And the security analysis demonstrates that MLCT can achieve the five targets security goals. It is expected that MLCT can contribute to the design and development of contact tracing schemes. © 2022 John Wiley & Sons, Ltd.

A Review of Blockchain Based Solutions for Fight Against Pandemics

Starting in late 2019, a highly infectious coronavirus disease spread rapidly to all over the world and caused many deaths worldwide. This disease known as COVID-19 caused more than 180 million cases including more than 4 million deaths. Numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. We can solve some of the problems of the pandemic with new technologies like blockchain, therefore we can control its spread until an effective and affordable vaccine is found. Blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. When ill people are detected, it is possible to quickly and accurately share their diagnostic information and clinical presentation with blockchain. We can also hide patients' identity while sharing that information. Anonymization is supported in blockchain and it's stronger than other techniques. Moreover, blockchain is transparent so that every disease event can be kept tabs on transparently. There are a lot of blockchain solutions in literature which is proposed recently to combat COVID-19-like pandemics. In this study, we aim to give the details of blockchain and why we can use it for fight against pandemics and review proposed solutions. We summarized the problems of pandemic and the benefits of blockchain technology. We highlighted the defects of solutions in literature and propose alternatives. © 2021 IEEE

Impact of Social Networks Safety on Marketing Information Quality in the COVID-19 Pandemic in Saudi Arabia

The study aimed to investigate the impact of social networks safety (SNS) on the marketing information quality (MIQ) during the COVID-19 pandemic in Saudi Arabia. The study examines the statistical differences in social networks safety SNS and marketing information quality MIQ according to the demographics such as age, sex, income, and education. For this study purpose, information security and privacy are two components of social networks safety. The research materials are website resources, regular books, journals, and articles. The population includes all Saudi users of social networks. The figures show that active users of the social network reached 25 Million in 2020. The snowball method was used and sample size is 500 respondents and the questionnaire is the tool for the data collection. The Structural Equation Modelling SEM technique is used. Convergent Validity, Discriminate Validity, and Multicollinearity are the main assumptions of structural equation modeling SEM. The findings show the high positive impact of SNS networks safety on MIQ and the statistical differences in such variables refer to education. Finally, the study presents a set of future suggestions to enhance the safety of social networks in Saudi Arabia.

To download, or not to download, that is the question: Investigating Belgian residents' motivation to download or not download the COVID-19 contact-tracing app Coronalert

This research explores COVID-19 contact-tracing apps (CTAs) from a citizen perspective by looking at the meaning they attribute to CTAs and their motivation to not use such kinds of apps. As such, it looks at the Belgian CTA Coronalert and semi structured interviews were used to investigate Belgian residents' opinions. What emerged from the interviews is that the meaning participants attribute to the CTA Coronalert is different from the meaning the app itself has and the meaning the government gave to the app. The app was created as a safe and privacy-preserving solution, however, participants expressed concern over privacy violations and lack of data transparency. © 2021 IEEE.

A Survey on Security and Privacy Issues in Contact Tracing Application of Covid-19.

In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy.

Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users' mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users' mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.