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A Blockchain-Based Privacy Sensitive Data Acquisition Scheme During Pandemic Through the Facilitation of Federated Learning
Abstract
New diseases (e.g., monkeypox) are showing up and taking the form of a pandemic within a short time. Early detection can assist in reducing the spread. However, because of privacy-sensitive data, users do not share it continually. Thus, it becomes challenging to employ modern technologies (e.g., deep learning). Moreover, cyber threats encircle both communication and data. This paper introduces a blockchain-based data acquisition scheme during the pandemic in which federated learning (FL) is employed to assemble privacy-sensitive data as a form of the trained model instead of raw data. A secure training scheme is designed to mitigate cyber threats (e.g., man-in-the-middle-attack). An experimental environment is formulated based on a recent pandemic (i.e., monkeypox) to illustrate the feasibility of the proposed scheme.
... 43 These CNNs include ResNet-18, MobileNet, NasNetMobile, GoogLeNet, EfficientB0, and ShuffleNet, where the highest performance was achieved with MobileNet. The same dataset (MSLD) was used in 44 A further dataset was introduced called Monkeypox Skin Image Dataset (MSID) 47 and was utilized in a number of studies. For example, the study 3 proposed a new approach for fine-tuning customized CNN layers for identifying monkeypox disease from photos utilizing AI-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS). ...
... It is worth mentioning that the MSLD dataset has two editions: the original version and the augmented edition. The authors of reference 44 used the augmented edition of the dataset which contains versions of augmented images in both training and testing sets which led to this very high accuracy with only features of ResNet-18. In other words, the authors trained their model with augmented images that has close augmented replicas in the testing data which definitely lead to a very high-performance results. ...
Objective
Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named “Monkey-CAD” that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately.
Methods
Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features’ sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers.
Results
Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively.
Conclusions
Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.
... Federated Learning (FL) is an innovative technology that enables models to be trained on numerous decentralized devices or servers, ensuring that data remains localized [5]. By allowing on-device training without sharing raw data, FL fosters collaborative model training among organizations, preserving privacy, and tapping into collective insights from distributed datasets [6]. In FL, only model parameters are shared, not raw data [7]. ...
Smart cities embrace unmanned autonomous vehicles (UxVs) for urban mobility and addressing challenges. UxVs include UAVs, UGVs, USVs, and UUVs, empowered by AI, particularly deep learning (DL), for autonomous missions. However, traditional DL has limitations in adapting to dynamic environments and raises data privacy concerns. Limited data availability and starting from scratch to adapt to a new environment during missions pose challenges. Additionally, cyber threats, particularly in terms of communication and data security, can jeopardize the missions performed by UxVs. This paper proposes a federated transfer learning scheme for UxVs, sharing prior knowledge and training with limited data while ensuring security through blockchain. Domain adaptation with maximum mean discrepancy enhances the DL model's performance in target domains. The proposed scheme's feasibility is demonstrated in an empirical environment, and it outperforms existing works.
... Other new diseases are emerging and, in a short time, can become pandemics. In [106], authors suggest a blockchain-based safe data aggregation approach based on a case study (i.e., monkeypox) in which data are collected utilising FL throughout the pandemic. ...
Recently, innovations in the Internet-of-Medical-Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications.
... The transfer of sensitive medical images raises privacy concerns. In [40], a secure data aggregation scheme was proposed to minimize cyber threats. Instead of transmitting raw data, blockchain-based data acquisition and federated learning were employed to assemble the data in the form of trained models. ...
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread.
... Therefore, a TL-based model also needs to be evaluated with various optimizers on different datasets in order to understand the model's stability as well. Most of the previous research also did not provide any clear explanation as to whether they had used generalization and regularization approaches (Eid et al., 2022;Haque, Islam, Islam and Ahsan, 2022;Islam & Shin, 2022;Sahin, Oztel, & Yolcu Oztel, 2022). Therefore, it is also not clear if their proposed model is suffering from overfitting issues or not, even though the reported accuracy is much higher (Akin et al., 2022;Haque, Ahmed, Nila, Islam et al., 2022). ...
Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.
The COVID-19 pandemic has brought human life to a startling halt around the world from the moment it emerged and took thousands of lives. The health system has come to the point of collapse, many people in the world have died from being infected, and many people who have survived the disease have had permanent lung damage with the spread of COVID-19 in 212 countries and regions. In this study, an answer is sought to diagnose the disease-causing virus through Artificial Intelligence Algorithms. The aim of the study is to accelerate the diagnosis and treatment process of COVID-19 disease. Enhancements were made using Deep Learning methods, including CNN, VGG16, DenseNet121, and ResNet50. For this study, the disease was detected by using X-Ray images of patients with and without COVID-19 disease, and then it was evaluated how to increase the accuracy rate with the limited available data. To increase the accuracy rate, the results of data augmentation on the image data were examined and the time complexity of the algorithms with different layers was evaluated. As a result of the study, it was seen that data augmentation increased the performance rate in all algorithms and the ResNet50 algorithm was more successful than other algorithms.
Monkeypox- a zoonotic disease caused by the monkeypox virus, an orthopoxviruses family member. Recently monkeypox cases are increasing at an alarming rate in the US and worldwide. Health care professionals should keep a high index of suspicion for the disease in anyone with new onset fever, a vesicular or pustular rash with central umbilication, and lymphadenopathy. Such patients should be isolated at home or the hospital to prevent secondary transmission. The cases are typically self-limited, and most people only need home supportive care. However, as recommended by CDC, immunocompromised patients, pregnant patients, and children younger than eight years should be offered pre- or post-exposure prophylaxis with vaccines. The current outbreak explicitly targets a cohort of homosexual and gay patients. The role of sexual transmission of the virus needs to be explored further. Patients with severe symptoms or respiratory complications can also be treated with antivirals such as ecovirimat (TPOXX) and brincidofovir or with intravenous vaccinia immune globulin (VIGIV).
Background
SARS-CoV-2 is a novel virus that causes coronavirus disease-19 (COVID-19). Many antiviral and immunomodulatory drugs can be used as a potential treatment. Doxycycline combined to Zinc can play a pivotal role to protect against SARS-CoV-2 infection
Objective
This study aims to assess the efficacy of a combination treatment of Doxycycline and Zinc, in primary prevention of COVID-19 infection in Tunisian Health Care Workers (HCWs) compared to two control groups.
Methods
We conducted a prospective randomized double-blind clinical trial over five months to determine the efficacy of a combination preventive treatment dose of Doxycycline (100 mg/day) and Zinc (15 mg/day), compared to a single-dose treatment with Doxycycline versus a placebo. The effectiveness of preventive treatment was measured by the significant decline in the number of cases of COVID-19 infection and/or a decrease in the viral load determined by SARS-CoV-2 cycle threshold (Ct) value using RT-PCRs test.
Results
We detected a significant decrease of SARS-CoV-2 infection in group who received both Doxycycline and Zinc compared to other participants. We are also demonstrated that COVID-19 infection was not associated with diabetes (p=0.51), nor with hypertension (p=0.99), asthma (p=0.52) and chronic obstructive pulmonary disease (p=0.27).
Conclusions
Our finding indicated that preventive therapy reduced the risk of SARS-CoV-2. These results suggest that the combination of Doxycycline and Zinc has a protective effect in SARS-CoV-2 infection.
Contagious disease pandemics present a significant threat worldwide in terms of both human health and economic damage. New diseases emerge annually and place enormous burdens on many countries. Additionally, using humans to handle pandemic situations increases the chances of disease spreading. Therefore, various technologies that do not directly involve humans should be employed to handle pandemic situations. The Internet of Drones (IoDT), artificial intelligence (AI), and blockchain are emerging technologies that have revolutionized the modern world. This paper presents a blockchain-based AIempowered pandemic situation supervision scheme in which a swarm of drones embedded with AI is engaged to autonomously monitor pandemic outbreaks, thereby keeping human involvement as low as possible. A use case based on a recent pandemic (i.e., COVID-19) is discussed. Two types of drone swarms are used to handle multiple tasks (e.g., checking face masks and imposing lockdowns). A lightweight blockchain is considered to handle situations in remote areas with poor network connectivity. Additionally, a two-phase lightweight security mechanism is adopted to validate the entities in the proposed scheme. A proof of concept is established using an experimental environment setup and dataset training. An analysis of the experimental results demonstrates the feasibility of the proposed scheme.
This paper presents a blockchain enabled secure data acquisition scheme utilizing an Unmanned Aerial Vehicle (UAV) swarm where data are collected from internet of things (IoT) devices and subsequently, forwarded to the nearest server through the UAV swarm. Before initiating data acquisition, the UAV swarm shares a shared key with IoT devices in order to maintain communications. However, prior to transmitting data, IoT devices encrypt the data and forward it to the UAV swarm. Upon receiving the data, the UAV swarm implements a two-phase validation utilizing the π-hash bloom filter and the digital signature algorithm to validate the sender; in addition, prior to forwarding data to the nearest server, it performs encryption. However, before adding data in blockchain, consent from all validators is required. Finally, the data are stored in blockchain with the approval of validators. A security analysis is performed to demonstrate the feasibility of the proposed scheme. Finally, the effectiveness of the proposed scheme is manifested through implementation and simulation. The security analysis and the performance results show that UAV assist IoT devices both in terms of connectivity and energy consumption, and provides security against the threats mentioned in the paper.
The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 62 state-of-the-art robust training methods, all of which are categorized into five groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we perform an in-depth analysis of noise rate estimation and summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies.
This letter presents a federated learning-based data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.
The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services by deploying containerized application instances on edge servers situated near end users. MEC servers are, however, susceptible to various types of failures such as communication link failures, hardware failures and so on. A fault recovery strategy determines which MEC servers to utilize to re-deploy application containers in the event of a failure. In this work, we propose a two-fold fault recovery strategy characterized by application priority. We propose a Formal Methods driven local recovery strategy for high-priority applications. We use Stochastic Multi-Player Games as a Formal Model to characterize the interactions between the different components in an MEC environment. We use objectives specified in Probabilistic Alternating-Time Temporal Logic with a Probabilistic Model Checker to derive recovery strategies considering all possible execution scenarios of the model. For lower priority applications, we resort to a global recovery strategy by designing a greedy heuristic considering each server's failure probability. We use benchmark datasets to validate our approach. Experimental results show an average 14\% reduction in latency with our approach in comparison with other state-of-the-art methods.
The modern era is filled with smart entities (e.g., smart vehicles) that have both sense and actuate capabilities. These entities can collect lots of data during their functional period and these data can be utilized for the wellbeing of citizens. However, these data are very sensitive raising issues like privacy. Moreover, network scarcity, bandwidth consumption, etc. can worsen the circumstance. Federated learning (FL), internet of drones (IoD), and dew computing (DC) are revolutionary technologies that can be engaged to mitigate the aforementioned challenges. An FL-based computing paradigm is initiated over the dew computing to process road-related data to bring efficiency in the applications (e.g., finding parking locations) utilizing IoD. An experimental environment is established containing a traffic dataset as a proof of concept. The experimental results exhibit the feasibility of the proposed scheme.
Internet of Things (IoT) established the foundation of the smart world via its diverse functionalities (e.g., remote data acquisition, automated task execution). This trend continues to military applications too by introducing internet of military things (IoMT). Due to being at infancy level, new technologies require to move forward and assist IoMT to gain maturity. Mixed reality (MR) can be a potential technology which fuses the virtual and actual world. MR can improve the quality of service (QoS) in terms of inventory management, remote mission handling, battlefield assistance, etc. However, data among these applications is surrounded by cyber threats (e.g., illegal data modification, unauthorised data access). Blockchain is another promising technology which brings security in the distributed world. A content sharing scheme for MR application is proposed on the top of blockchain to bring security in the multi-user environment in IoMT. A smart contract is employed to manage security in accessing data by different users. Moreover, an experimental environment is set to observe the performance of the proposed scheme. The analysis manifests that proposed scheme maintains security without affecting the regular performance.
To stay up to date with world issues and cutting-edge technologies, the newspaper plays a crucial role. However, collecting news is not a very easy task. Currently, news publishers are collecting news from their correspondents through social networks, email, phone call, fax, etc. and sometimes they buy news from the agencies. However, the existing news sharing networks may not provide security for data integrity and any third party may obstruct the regular flow of news sharing. Moreover, the existing news schemes are very vulnerable in case of disclosing the identity. Therefore, a universal platform is needed in the era of globalization where anyone can share and trade news from anywhere in the world securely, without the interference of third-party, and without disclosing the identity of an individual. Recently, blockchain has gained popularity because of its security mechanism over data, identity, etc. Blockchain enables a distributed way of managing transactions where each participant of the network holds the same copy of the transactions. Therefore, with the help of pseudonymity, fault-tolerance, immutability, and the distributed structure of blockchain, a scheme (termed as NEWSTRADCOIN) is presented in this paper in which not only news can be shared securely but also anyone can earn money by selling news. The proposed NEWSTRADCOIN can provide a universal platform where publishers can directly collect news from news-gatherers in a secure way by maintaining data integrity, without experiencing the interference of a third-party, and without disclosing the identity of the news gatherer and publishers.
This paper introduces a blockchain-based secure healthcare scheme in which health data (HD) are collected from users via an unmanned aerial vehicle (UAV) and stored on the nearest server. In the proposed scheme, a UAV first establishes a relation with body sensor hives (BSHs) via a token and then shares a shared key with BSHs to enable low-power secure communication. After retrieving the HD, the UAV decrypts the encrypted HD (encrypted by a BSH) using the shared key and exercises a two-phase authentication mechanism. Upon successful validation, the UAV transmits the HD to the nearest server to store it securely in blockchain. A security analysis is discussed to show the feasibility of the proposed secure healthcare scheme. Finally, the performance of the proposed scheme is investigated through simulation and implementation. The security and the performance analysis demonstrate that the proposed scheme supports better assistance to BSHs while maintaining security.
Internet of things (IoT), mobile edge computing (MEC), and unmanned aerial vehicle (UAV) have attracted significant attention in both industry and academic research. By consolidating these technologies, IoT can be facilitated with improved connectivity, better data transmission, energy saving, and other advantages. However, the communication between these entities is subject to potential cyber threats. In addition, the integrity of the data must be maintained after storing into local storage. Blockchain is a data structure that supports features like pseudonymity, data integrity etc. This paper represents a blockchain based data acquisition process in which information is gathered from IoTs using UAV as a relay and is securely kept in blockchain at MEC server. In the proposed scheme, data are encrypted prior to transfer to MEC server with the assistance of a UAV. Upon receiving the data, MEC server validates the data and the identity of the sender. Successful validation is followed by stocking of the data into blockchain, subsequent to obtaining consent from the validators. Security analysis is conducted in order to show the feasibility of the proposed secure scheme. Finally, the performance of the proposed scheme is analyzed via simulation and implementation.
In order to stay up to date with world issues and cutting-edge technologies, the newspaper plays a crucial role. However, collecting news is not a very easy task. Currently, news publishers are collecting news from their correspondents through social networks, email, phone call, fax etc. and sometimes they buy news from the agencies. However, the existing news sharing networks may not provide security for data integrity and any third party may obstruct the regular flow of news sharing. Moreover, the existing news schemes are very vulnerable in case of disclosing the identity. Therefore, a universal platform is needed in the era of globalization where anyone can share and trade news from anywhere in the world securely, without the interference of third-party, and without disclosing the identity of an individual. Recently, blockchain has gained popularity because of its security mechanism over data, identity, etc. Blockchain enables a distributed way of managing transactions where each participant of the network holds the same copy of the transactions. Therefore, with the help of pseudonymity, fault-tolerance, immutability and the distributed structure of blockchain, a scheme (termed as NEWSTRADCOIN) is presented in this paper in which not only news can be shared securely but also anyone can earn money by selling news. The proposed NEWSTRADCOIN can provide a universal platform where publishers can directly obtain news from news-gatherers in a secure way by maintaining data integrity, without experiencing the interference of a third-party, and without disclosing the identity of the news gatherer and publishers.
Multi-access Edge Computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the Radio Access Network (RAN). MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.
Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified vgg16
- M M Ahsan
- M R Uddin
- M Farjana
- A N Sakib
- K A Momin
- S A Luna
M. M. Ahsan, M. R. Uddin, M. Farjana, A. N. Sakib, K. A. Momin,
and S. A. Luna, "Image data collection and implementation of deep
learning-based model in detecting monkeypox disease using modified
vgg16," 2022. [Online]. Available: https://arxiv.org/abs/2206.01862
Monkeypox skin lesion detection using deep learning models: A preliminary feasibility study
- S N Ali
- M T Ahmed
- J Paul
- T Jahan
- S M S Sani
- N Noor
- T Hasan
S. N. Ali, M. T. Ahmed, J. Paul, T. Jahan, S. M. S. Sani, N. Noor, and
T. Hasan, "Monkeypox skin lesion detection using deep learning models:
A preliminary feasibility study," arXiv preprint arXiv:2207.03342, 2022.
Monkeypox skin lesion detection using deep learning models: A preliminary feasibility study
- ali
Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified vgg16
- ahsan