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.

Big data platforms

Big data refers to huge datasets which gives the advanced analysis about the system. Information is increasing nowadays due to more advanced technology, tools, and techniques. When there is a huge increase in information, it has some merits and demerits like maintaining the data, data visualization, and securing the data. Big data offers various tools and techniques to maintain and secure data. This chapter focuses on two important parts of big data, namely, visualization and security. It is really challenging task to visualize data in simple way but big data makes easy to understand and interpret. This study explains different techniques such as 1D, 2D, and multidimensional with current pandemic case study on COVID-19 with a diagrammatic representation for easy understanding of the techniques. Data visualization makes communication insights from data through visual representation. Its main aim is to still the datasets into visual graphics to allow easy understanding with complex relationships within the data. Data visualization promotes that creative data exploration [3]. Data visualization is applied in every field of knowledge. Researchers use computer techniques to model complex events and visualize phenomena that cannot be observed directly such as weather patterns and medical conditions. Today, the world is facing pandemic situations like COVID-19, and big data has played vital role in analysis and predictions [1]. Visualization made great help to society to visualize the patients in different areas or in the world. By using plotly data visualization analysis, different datasets of COVID-19 are covered in this study. Today, almost every institute is thinking big data as they are seeing growth of big data using Hadoop, Spark, Pig, etc. HDFS (HADOOP Distributed File System) is distributed file system. All types of files can be stored in the HDFS. Biggest challenge for big data from a data security point of view is the protection's user details. As per studies, it is demonstrated the flow of big data is in three layers: incoming, stored, and outgoing data. The organizations may face the problem of encryption while storing huge data. There are some major issues while handling big data about security such as data breach, data access, and privacy violations. Due to larger data sets, there may serious data branches problem which can result more devastating consequences than normally seen in any system. This chapter covers part of data breaches in the year 2018 to 2019 and conclusion of 2020. This study also demonstrates an investigation about virtual reality which radically changes the world of big data. © 2022 Scrivener Publishing LLC.

Why Zoom Is Not Doomed Yet: Privacy and Security Crisis Response in the COVID-19 Pandemic

The COVID-19 pandemic not only fueled the explosive growth of Zoom but also led to a major privacy and security crisis in March 2020. This research examines Zoom's response to this privacy and security crisis with the aid of a producer's perspective that aims to direct attention to institutional and organizational actors and draws on theories of privacy management and organizational crisis communication. We primarily use data from 14 weekly Ask Eric Anything webinars from April 8 to July 15, 2020, to illustrate the strategies of Zoom's crisis response, especially organizational representation, the contours of its analytic account acknowledging and minimizing responsibility, and patterns of corrective and preventive action for user education and product improvement. Results demonstrate the usefulness of the producer's perspective that sheds light on how Zoom navigated the privacy and security crisis. Special attention is paid to the mobilization of networks of executives, advisors, consultants, and clients for expertise, endorsement, and collaboration. It is argued that Zoom's response strategies have contributed to Zoom's organizational mission and culture and reframed the crisis from a growing pain to a growth opportunity relating to privacy and security. Zoom's nimble, reasonable, collaborative, interactive yet curated organizational response to the privacy and security crisis can be seen as an unintended consequence of its sudden rise amid a global pandemic. It offers a useful model for tech firms' crisis response at a crucial moment for the tech industry around the world. © 2023 SAGE Publications.

A Novel Approach to Ensure Security and Privacy While Using QR Code Scanning in Business Applications

One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged device-based barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality. © 2022 IEEE.

A Study On Security and Privacy Risks of Self-Disclosure On Social Networking Sites During COVID-19 Pandemic

Social networking sites (SNSs) contain a large amount of information that has been self-disclosed by users around the world because it provides a platform for millions of users to express their feelings, emotions, and even deepest thoughts. Some of these information are sensitive and private and can be used by hackers to launch social engineering attacks against the user or the company the user works for. Due to the physical restrictions imposed by the COVID-19 pandemic, more people turned to social media to stay connected with each other and they spent more time on social media and disclosed much more information than the pre-COVID pandemic. The objective of this research is to study the potential security risks and privacy concerns brought by the disclosed information on SNSs during the COVID-19 pandemic. We developed an automated tool to collect and analyze publicly accessible data from Twitter API using some personal keywords such as birthday, anniversary, mental health, suicide etc. to investigate the impact of the COVID-19 pandemic on the disclosed sensitive information. © 2022 IEEE.

CPSOCKS: Cross-Platform Privacy Overlay Adapter Based on SOCKSv5 Protocol

In figures, the cybersecurity landscape is one of the most across-the-border impactable trends in the last years, especially after the begging of the COVID-19 pandemic. Therefore, by the end of Q4 of 2021, more than 281 million people have been victims of data breaches and cyber-threads, costing more than $42.96 million per day. A possible explanation is that most network operators do not provide any mechanism that blocks path tracing. Almost anybody with above-average network security knowledge can use public path tracing tools such as traceroute, enabling malicious users and thread factors to craft sophisticated cyber-attacks easily. Therefore, this paper proposes a cross-platform privacy overlay over the SOCKSv5 protocol. We evaluate the proposed solution in terms of latency, average throughput, and transfer rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Use of Blockchain Technology to Protect Privacy in Electronic Health Records- A Review

Patients' medical files are electronically preserved and accessible through a network such as Electronic Health Records (EHRs). Numerous opportunities exist for EHRs to enhance patient care, clinical practice performance indicators, and potential future clinical research contributions. The techniques used to preserve EHRs have proved incredibly unsafe in the contemporary era of smart homes and urban areas. Data can be easily accessed by hackers and unauthorized third parties. Furthermore, the data is not accessible to patients or healthcare practitioners. These plans cannot balance the accessibility and security of the data. But with blockchain, these issues can be resolved. Any application created utilizing blockchain technology is secure and inaccessible to unauthorized parties thanks to the three critical characteristics of the technology: Security, Decentralization, and Transparency. In a blockchain network, it is nearly difficult to manipulate data. This research work utilizes blockchain technology to deploy EHRs and improve their security and privacy. With its decentralized structure and cryptographic techniques, blockchain technology will maintain control over who gets access to information. Furthermore, it will maintain a balance between accessing data and privacy. The advanced aspects of the EHR system are handled by this research using smart contracts. The comprehensive healthcare management solution across a network can incorporate several sectors, such as billing and transportation. A website program can be combined with it to increase interactivity. By adding pharmacists to the system as a participant, EHRs can help them track medical sales. © 2023 IEEE.

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.

Near Real-Time Federated Machine Learning Approach Over Chest Computed Tomography for COVID-19 Diagnosis

During the COVID-19 pandemic, artificial intelligence (AI) plays a major role to detect and distinguish between several lungs diseases and diagnose COVID-19 cases accurately. This article studies the feasibility of the federated learning (FL) approach for identifying and distinguishing COVID-19 X-ray images. We trained and tested FL components by using the data sets that collect images of three different lungs conditions, COVID-19, common lungs and viral pneumonia. We develop and evaluate FL model horizontally with same parameters and compare the performance with the classic CNN model and the transfer learning approaches. We found that FL can quickly train artificial intelligence models on different devices during a pandemic, avoiding privacy leaks that may be caused by such a high resolution personal and private X-ray data. © 2022, Springer Nature Singapore Pte Ltd.