The First Wave of COVID-19 in Forensic Psychiatry: A Rapid Review Series

At the beginning of the pandemic, experts expected an increasing number of hospitalizations in forensic settings, uncontrollable outbreaks of COVID-19, and deterioration of mental health of residents within institutions. Certain publications corroborated these concerns;however, no synthesis of the results of empirical publications at the initial stage of the pandemic has yet been conducted. Three rapid reviews were conducted on these topics. Besides almost a two-fold decrease in the total number of urgent consultations/hospitalizations, there were no changes in the number of involuntary hospitalizations, suicide attempts, and psychoses. The COVID-19 morbidity and mortality rates in secure institutions were compatible with the general population. However, the lockdown period was associated with a significant increase in self-harm in secure settings.

Privacy-Preserving Social Media with a Disclosure

Social Media are an important communication tool in today's society. In recent years, many events have been held online due to COVID-19, making Social Media an even more important communication tool. However, it is difficult to explicitly imagine the recipients of messages when posting on Social Media and there is a tendency to provide information easily, leading to the existence of inappropriate postings that the user does not intend. Furthermore, it is difficult to disclose information for anonymous posting on Twitter. This cause the link problem between the posts. In our proposal, we realize a way to solve these problems by realizing a Social Media that allows both unlinkable posting and disclose posting. Specifically, unlinkable posts can be changed to named posts, and when the name is changed, it is guaranteed that the person who posted the anonymous post was really the anonymous writer and that the anonymous writer cannot be identified from the anonymous post. We introduced randomized pseudonyms to prevent the viewer from checking a post text based only on the posting name without checking the contents of the posting. We also show how to prevent the attack on our proposed scheme by using hiding property and binding property of the commitment scheme. In addition, we implement the proposed scheme and describe the changes between our proposed scheme and regular post in posting time, publication time, and verification time.

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).

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory.

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user's data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

PSImple: Practical Multiparty Maliciously-Secure Private Set Intersection

Private set intersection (PSI) protocols allow a set of mutually distrustful parties, each holding a private set of items, to compute the intersection over all their sets, such that no other information is revealed. PSI has a wide variety of applications including online advertising (e.g., efficacy computation), security (e.g., botnet detection, intrusion detection), proximity testing (e.g., COVID-19 contact tracing), and more. Private set intersection is a rapidly developing area and there exist many highly efficient protocols. However, almost all of these protocols are for the case of two parties or for semi-honest security. In particular, despite the high interest in this problem, prior to our work there has been no concretely efficient, maliciously secure multiparty PSI protocol. We present PSImple, the first concretely efficient maliciously-secure multiparty PSI protocol. Our construction is based on oblivious transfer and garbled Bloom filters, and has a round-optimal online phase. To demonstrate the practicality of PSImple, we implemented it and ran experiments with up to 32 parties and 220 inputs. We show that PSImple is competitive even with the state-of-the-art concretely efficient semi-honest multiparty PSI protocols. Additionally, we revisit the garbled Bloom filter parameters used in the 2-party PSI protocol of Rindal and Rosulek (Eurocrypt 2017). Using a more careful analysis, we show that the size of the garbled Bloom filters and the number of oblivious transfers required for malicious security can be significantly reduced, often by more than 20%. These improved parameters also imply a better security guarantee, and can be used both in the 2-party PSI protocol of Rindal and Rosulek and in PSImple.

Ambivalent Resonance: Advocacy for Secure Status for Migrant Farm Workers in Spain, Italy and Canada during the COVID-19 Pandemic

Drawing on insights from scholarship on contentious action frames, this article examines the framing of demands for social justice for migrant farmworkers in Spain, Italy and Canada during the COVID-19 pandemic. We focus particularly on how activists in each country aligned their action frames with prevalent public discourses on the essential contribution migrants make to agricultural production, the need to guarantee "health for all,” and "increased vulnerability” of migrants' lives during the global health crisis. Using these diagnostic frames, activists in the three countries called for secure legal status for all migrants. Drawing on the literature on contentious action frames, we then analyze if action frames advanced by activists during the COVID-19 pandemic "resonated” with the understanding of these issues by policymakers. We challenge an approach to understanding resonance in binary terms as either present or absent. Instead, we introduce the notion of "ambivalent resonance” to draw attention to the fact that some frames are accepted only partially or only by some policymakers but not the others, as was the case in the three countries under study. We then situate this ambivalent resonance in the context of immigration priorities and recent trends in immigration policy development in these three countries and suggest that activists can build on ambivalences to advance migrant rights to status © 2023, Studies in Social Justice.All Rights Reserved.

PRISC: Privacy-Preserved Pandemic Infection Risk Computation through Cellular Enabled IoT Devices

The pandemics such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this paper, we develop a novel privacy-preserved pandemic tracing system, PRISC, to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: a mobile network operator, a social network provider, and the health department. The physical contact records between users are obtained by the mobile network operator from the users’cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for datasets with 100,000 users. IEEE

Automated Building of an Environment for Secure Software Development in Web Technologies Courses

This paper investigates the automated building of verified software environments that can be used in university courses. Over the last couple of years, it has become obvious that using online environments, video meetings, virtual lectures, online teaching, and learning is not a matter of choice. The coronavirus pandemic forced all parts of the education systems, and even of life, to go online. Deepening the research and development in the software automation field can lead to using various ways to allow university students and learners to "get in touch with” the real-world problems in software development. We developed such an approach by defining the steps, developing and evaluating specific automation processes of building an environment for secure software development. We defined both the functional and nonfunctional requirements for such a system, and the next major steps in development, such as virtualization setup (kvm), virtual environment (virtual machines) definition and creation, database configuration and management of user settings, have been defined and developed. The evaluation is performed according to the specifics defined in the Web technologies course, but the results and use are not limited to that course alone. In conclusion, the results of the evaluation conducted in a laboratory setting have been presented and appropriate scenarios, applications and future work have been defined. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The SECURE STAIRS framework: preliminary evaluation of the impact on therapeutic climate in a secure children's home (SCH)

PurposeThe SECURE STAIRS framework for integrated care is a trauma-informed approach to supporting staff and young people within the Children and Young People's Secure Estate (CYPSE) in the UK. Within secure settings, therapeutic climate is a concept that encapsulates an individual's perception of safety, connectedness with others and level of support within the environment. To support evaluation of the SECURE STAIRS framework, a Secure Children's Home (SCH) within the North East of England examined therapeutic climate for staff and young people annually using the Essen Climate Evaluation Schema (EssenCES) over a three-year period. This paper aims to present the findings.Design/methodology/approachOver the three years, a total of 71 young people and 214 staff EssenCES questionnaires were administered. Between 2020 and 2021, the setting also experienced significant changes resulting from the COVID-19 pandemic. Numbers of young people also decreased within the setting over the three-year period.FindingsResults indicated a positive trend for therapeutic climate sub-scores. For example, Experienced Safety for young people significantly increased from 2020 to 2021. Additionally, therapeutic hold for staff was significantly higher in 2020 and 2021 in comparison to 2018.Originality/valueFindings are discussed in relation to implementation of the SECURE STAIRS framework and providing trauma-informed care for vulnerable young people within secure settings. Implications for practice are explored.

Administration of Vaccine Mechanism for COVID-19 Using Blockchain

Information related to Covid-19 either it is vaccination status of the country or the active Covid-19 cases both are the confidential matters. The privacy is utmost important concern in pandemic situation to secure access of patient vaccine data. Blockchain technique is one of the good techniques that affirm the privacy and data security. The consensus mechanisms in blockchain confirm that data stored in it, is authentic and secured. Proof of Work is one of the consensus algorithms, where miners in the blockchain network solves the puzzle and receive the reward accordingly. The difficulty level of the puzzle decides the security of the data in the network. Hence, this paper proposes blockchain based framework to store the vaccination data of patient by enhancing security using proof of work consensus algorithm. The performance of the proposed framework is measured on different level of difficulties, corresponding to time. The result shows that higher the difficulty level, take more time to solve the puzzle, results in more secure data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

REMICARE—Medicine Intake Tracker and Healthcare Assistant

An automated reminder mechanism is built in this Android-based application. It emphasizes the contact between doctors and patients. Patients can set a reminder to remind them when it is time to take their medicine. Multiple medications and timings, including date, time, and medicine description, can be programmed into the reminder by using image processing. Patients will be notified through a message within the system, as preferred by the patients. They have the option of looking for a doctor for assistance. In this COVID-19 pandemic situation where nurses have to remind the patients in the hospitals to take their medications, our application can be useful, alerting the patient every time of the day when he/she has to take the medicine and in what amounts. Also, all the necessary tests report and prescriptions can be saved on the cloud for later use. Patients will be provided with doctor contact information based on their availability. Also, patients will be notified of the expiry date of the medicine, and the former history of the medicines can be stored for further reference. The proposed system prioritizes a good user interface and easy navigation. Image processing will be accurate and efficient with the help of powerful CNN-RNN-CTC algorithm. It also emphasizes on a secure storage of the user's data with the help of the RSA algorithm for encryption and the gravitational search algorithm for secure cloud access. We attempted to create a Medical Reminder System that is cost-effective, time-saving, and promotes medication adherence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effects of visual information on the face masked speech communication in Covid-secure classrooms

Acoustical measurements and speech intelligibility tests were carried out to investigate the effects of masks on speech communication experienced in real Covid-secure university classrooms during the pandemic. Face-masked speech levels and noise levels were measured to understand the acoustical effects of masks on speech sounds during 15 multiple lectures in 3 university classrooms. The speech intelligibility scores were also evaluated for lower and higher SNR (signal-to-noise ratio) conditions, and for with and without the presence of visual information conditions to investigate the effects of both the acoustic and visual signals in understanding speech communication in actual classroom situations. In the 3 active university classrooms the students experienced on average: speech levels of 55.1 dBA (σ = 5.5 dBA), noise levels of 42.3 dBA (σ = 3.9 dBA), and a speech-to-noise ratio of 12.8 dBA σ = 5.2 dBA). The mean SNR values at the listener's position for the 15 lectures varied from 3.6 dBA to 20.0 dBA. The use of a portable sound amplification system increases the face-masked speech levels mostly at mid and high frequencies (500–4 kHz), thus it can be more useful for achieving higher SNR values in classrooms. The presence of visual cues have little effect on achieving more higher speech intelligibility scores in higher SNR conditions. The present results show that visual obstruction of the talker's mouth decreases speech intelligibility scores by a maximum of 10% in lower SNR conditions, particularly at a SNR of 6 dBA or lower. © 2023 Elsevier Ltd

A Novel Approach of Smart Contract based Distributed Ledger Technology using Deep Learning Techniques to Secure Medical Images

Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset. © 2022 IEEE.

Multi-party Secure Comparison of Strings Based on Outsourced Computation

Data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask and Helmet Facial Detection for ATM Security Using Image Processing in Cloud Computing

As we have seen in lots of newspaper, news and many magazines that robbery became the common and main issue going on over the world. My biggest concern is on secure ATM transaction, where nowadays many ATM's get misleaded and robbery are occurring very easily. So, to handle this situation Image processing has been playing a major role to identify the person who are doing the transaction via ATM. Because has the covid cases are increasing many people wear mask and helmet and do misuse the ATM for illegal transaction, here where the image processing helps to differentiate the person wearing mask/helmet and without mask/helmet for the further ATM transaction. And secondly Cloud is also playing a major in storing the datas where it stores the static data to train the model and to detect the accuracy and stores the captured real time image in different folder so that we can easily identify the person with mask/helmet and without mask/helmet. © 2022 IEEE.

COVID-19: What can we learn from shared experiences of social distancing within secure services?

COVID-19 is an infectious disease that has spread across the globe with a social, economic and psychological impact that will undoubtedly change the world in which we live. Those working in mental health services will have experienced major changes in working practices, including navigating the challenges of infection control, whilst caring for some of the most vulnerable members of our society. There have been a number of publications considering many of the practicalities of the COVID-19 challenges in mental health. However, deeper reflections of philosophical issues regarding our own shared experiences have not yet been well covered in the professional press. This commentary describes some experiences of working within a low secure forensic service at the beginning of the COVID-19 pandemic in the UK. It aims to explore some of the key themes arising from this unprecedented situation, proposing areas for reflection and shared learning within the mental health inpatient community. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

CyberGeeksMY: Public Awareness on Website Vulnerability and Proposed Idea of 'Smuggy' Prototype

The needs on online-based activities are increasing tremendously as the worldwide is currently in COVID-19 pandemic situation and most people are being bounded to stay at home. Society is forced to learn and adapt with this new normal in all aspects of life from running businesses, conducting online classes and meetings, buying stuffs and communicating with families and friends. This causes high dependency on web applications. The idea of proposing 'Smuggy' prototype has emerged to provide relevant skillsets for web penetration testers specifically and web developers in general. In order to realize the idea, a survey was conducted to gauge for public awareness on website vulnerability as the first part of this research. The scope of survey is on public concerns when browsing reputable websites vs. unfamiliar new ones, which further down to know the reasons why the respondents feel as such. Another part of the survey was to find out the respondents' knowledge on related events of compromised websites and their encounter in discovering web vulnerabilities. With these data as research basis, 'Smuggy' prototype is designed with a focus to educate users about the OWASP top 10 web vulnerabilities so that the vulnerabilities found in today's web applications can be minimized. © 2022 IEEE.

ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve O ( log N log ( log N ) ) improvements compared to O N 2 in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.

The New Generation of Contact Tracing Solution: The Case of Morocco

Nowadays, the entire world is struggling to adapt and survive the global pandemic. Moreover, most countries had a hard time keeping up with the new mutations of COVID-19. Therefore, taking preventive measures to control the spreading of the virus, including lockdowns, curfews, social distancing, masks, vaccines, is not enough to stop the virus. However, using the new technologies to adapt the prevention measures and enhance the existing ones will be more efficient. Most countries have already developed their non-pharmaceutical interventions measures (NPIs), mainly contacts tracing solutions at the pandemic beginning. Using those mobile applications, the authorities were able to reduce the spreading of the virus. Nevertheless, the virus is evolving, mutating, and becoming more and more dangerous to survive. Therefore, these mobile applications have become less effective in facing the constant changes of the pandemic situation. To that end, the need for enhancing and evolving contact tracing became more urgent. The goal here is to control the spread of the new variants and keep up with the rapid changes happening around the world. In this paper, we will present a detailed view of the new solution built to take contact tracing to a new level, empowered by the Bluetooth Low Energy technology for communication, advanced encryption method for security and data privacy, as well as secured storage and data management to have a system capable of slowing the COVID-19 variants from spreading and save lives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Health and Cybersecurity in the Era of Artificial Intelligence

Since 2020, the world has been impacted badly by the pandemic situation that arose due to the coronavirus. Artificial intelligence plays a crucial role in the healthcare system, specifically identifying symptoms of disease with the help of various machine learning algorithms during the diagnosis stage. The identified symptoms in various diagnostic tests are used to predict the clinical outcome of early detection of diseases, which results in human life saving. Machine learning algorithms have been successfully used in automated interpretation. With the advanced technology of cybersecurity aspects, we can emphasize data protection for better results. Artificial intelligence can enhance the security of medical science data. Furthermore, they improvise cybersecurity techniques with machine learning technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.