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We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local dataset suffers from low accuracy, which can be improved by the aforementioned convergence and Ensemble Learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, Ensemble Learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a Differential Privacy-based privacy-preserving DNN with Transfer Learning for a local model generation to ensure minimal loss and higher efficiency at edge server. We conduct several experiments to evaluate the performance of our proposed framework.

Machine Learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this paper, we present a privacy-preserving machine learning (ML) framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using Differential Privacy (DP) and Radial Basis Function Network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model’s performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

Bangladesh's economy has grown at one of the fastest rates in the world owing to a demographic transition, stable socio economic conditions, foreign remittance and overall exports of domestic goods. As a whole, with the overall increase in industrialization and technological development, there are inevitably enormous prospects for nuclear energy development in Bangladesh's rapidly growing economic conditions. However, the radioactive waste produced by nuclear weapons, nuclear power plants and the production of medical isotopes is one of the most challenging wastes for modern society to manage.

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users' queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework. 2005-2012 IEEE. Scopus

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

Network function virtualization (NFV) allows edge devices to host different types of virtual network functions (VNFs) and support Computing in the Network. Therefore, an edge device can abstract multiple VNFs of different infrastructure providers (InPs) as services and serve them at different times to act as a tenant for different InPs. Deploying edge devices is expensive and difficult to manage. Hence, crowdsourced edge devices can be used. To ensure the quality and availability of the services and the privacy of sensitive data of the service stakeholders, it is extremely important to establish a service level agreement (SLA) between an edge device owner and an InP. For monitoring of SLA compliance and enforcing accountability, a trusted platform is required. The traditional NFV management authority cannot be trusted due to its centralized characteristics. In this article, we propose a blockchain-powered framework for the SLA compliance of edge devices offering VNFs as services and introduce a smart contract-driven framework to establish trust in edge-based NFV. We analyze the performance of the proposed framework in the context of a private blockchain network.

This work presents a review of nanocomposites of nickel oxides (NiO, nickel cobaltite NiCo2O4), nickel hydroxides (Ni(OH)2), layered-double hydroxides of Ni (LDH-Ni) with graphene, functionalized-g...

The study focuses on comparing technical efficiency and profitability between Boro rice farmers and maize farmers in Dinajpur district consisting of 150 randomly collected samples which are analyzed with statistical software STATA 12.0 version. Stochastic Frontier Analysis (SFA) is used to accomplish the first objective where it is found that the maize farmers are efficient compared to boro-rice farmers. The deviation of the mean technical efficiency is estimated at 0.09. In addition maize farmers are also more profitable than boro-rice farmers estimated with a benefit Cost Ratio (BCR) of1.08 is for boro-rice and 1.26 is for maize production. The net profit for boro-rice is estimated as 10,527.60 Tk./ha whereas 28,966.40 Tk./ha for maize producers. However mean technical efficiency of boro-rice (0.44) and maize (0.53) cultivation clearly indicates that better utilization of resources will raise the efficiency and profitability for both crops production. Res. Agric., Livest. Fish.8(1): 51-56, April 2021

Background: Vitamin D are needed for normal functioning of cardiovascular system, and its deficiency has been linked to different cardiovascular disorders, including hypertension. However, the relationship between vitamin D deficiency and ischaemic heart disease is not yet clear. The present study was intended to find out the association between serum vitamin D level and acute coronary syndrome (ACS). Methods: The cross-sectional analytical study was carried out in a tertiary care hospital of Dhaka, Bangladesh. Fifty-one consecutive ACS patients admitted into the Department of Cardiology of the hospital were considered as the cases and 51 apparently healthy subjects were considered as the controls. The exposure and outcome variables were serum vitamin D and ACS respectively. Vitamin D insufficiency and deficiency were defined as serum 25(OH) D

We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

Since December 2019, a de novo pattern of pneumonia, later named coronavirus disease 2019 (COVID-19), has caused grave upset throughout the global population. COVID-19 is associated with several comorbidities; thus, preventive and therapeutic strategies targeting those comorbidities along with the causative agent, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), seem imperative. In this state-of-the-art review, edible and medicinal mushrooms are featured in the treatment of SARS-CoV-2, COVID-19 pathomanifestations, and comorbid issues. Because this is not an original research article, we admit our shortcomings in inferences. Yet we are hopeful that mushroom-based therapeutic approaches can be used to achieve a COVID-free world. Among various mushroom species, reishi or lingzhi (Ganoderma lucidum) seem most suitable as anti-COVID agents for the global population.

Drones, or unmanned aerial vehicles, can be used for commercial services such as short-dis-tance delivery. In order to ensure quality services, multiple drone-based delivery service providers can be employed in delivery service systems. In this article, we address two very important and unexplored challenges of employing drones in delivery services. First, involving multiple drones from different service providers elevates the chance of collision during flights. Second, drones may hamper the privacy of citizens by unauthorized access to private and restricted areas. In order to solve the aforementioned issues, we propose a blockchain-based policy enforcement mechanism in the drone-based delivery service systems. At first, the mechanism will set policies to establish pre-allocated flight paths for different drones at different times to avoid collisions, and ensure the privacy of citizens by restricting their access to unauthorized areas. Later, the blockchain will enforce the policies to monitor compliance of the drone flights and identify non-compliant drone services to penalize corresponding service providers. We simulate a virtual drone-based delivery system with the Ethereum blockchain platform and examine the performance and feasibility of our proposed mechanism.

A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been discounted in recent works due to the volatility of low-level timing information. In this paper, we more carefully examine the extent to which packet timing can be used to facilitate WF attacks. We first propose a new set of timing-related features based on burst-level characteristics to further identify more ways that timing patterns could be used by classifiers to identify sites. Then we evaluate the effectiveness of both raw timing and directional timing which is a combination of raw timing and direction in a deep-learning-based WF attack. Our closed-world evaluation shows that directional timing performs best in most of the settings we explored, achieving: (i) 98.4% in undefended Tor traffic; (ii) 93.5% on WTF-PAD traffic, several points higher than when only directional information is used; and (iii) 64.7% against onion sites, 12% higher than using only direction. Further evaluations in the open-world setting show small increases in both precision (+2%) and recall (+6%) with directional-timing on WTF-PAD traffic. To further investigate the value of timing information, we perform an information leakage analysis on our proposed handcrafted features. Our results show that while timing features leak less information than directional features, the information contained in each feature is mutually exclusive to one another and can thus improve the robustness of a classifier., Comment: Accepted in Privacy Enhancing Technologies Symposium (PETS) 2020. Code for reproducing the results are available in https://github.com/msrocean/Tik_Tok/

We propose a hierarchical blockchain-based platform for ensuring the integrity of smart city Internet-of-Things (IoT) data and blockchain interoperability in this paper. The well-defined structural hierarchy of managing organizations in a smart city face several data management issues. The integration of emerging technologies, such as (IoT), has introduced more complexity to data maintenance in the smart city. At present, cloud-based information systems have become the norm for managing IoT data. Due to the centralized administrative control, the cloud-based information management is untrusted. Hence, it fails to guarantee the integrity of IoT data and traceability of operations to achieve transparency within the smart city organizations. Adoption of blockchain-based information systems can be viable solution to address these challenges. However, it introduces a new challenge of interoperability of transactions among different smart city organizations. In this paper, we introduce a decentralized hierarchy of blockchains, named Blockchain-of-Blockchains (BoBs), for ensuring data integrity and blockchain interoperability at the same time. We implement the proposed concept using Hyperledger Fabric and Ethermint as a proof-of-concept for evaluating the performance of the proposed concept. 2022 Elsevier Inc. This publication was made possible by NPRP grant NPRP11S-1227-170135 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors ( www.supplyledger.qa ). Scopus

Alzheimer’s Disease (AD) susceptibility has been soared during global pandemic of coronavirus disease 2019 (COVID-19). People of advanced ages, especially those over sixty years old are more vulnerable to AD and COVID-19 conundrum.

In this paper, we present a privacy-preserving service selection framework for cloud-based service systems. In the cloud-based service system, a cloud provider selects the best service from a set of services based on their Quality-of-Service (QoS) information. The QoS information of services is sensitive from the service provider’s point of view. We claim that the service selection process in the cloud can be biased. A service provider can bribe a dishonest employee of the cloud provider for taking unfair advantage during a service selection process. Therefore, it is important to execute the service selection tasks keeping QoS information private. We use a fully homomorphic encryption ( F H E ) scheme in this paper for encrypting QoS values. Service selection task is performed by the cloud provider on encrypted QoS values to ensure privacy. In order to reduce computation overhead, we propose a MapReduce model for parallel execution. We conduct several experiments to evaluate the performance of our proposed privacy preserving service selection framework using synthetic QoS dataset.

We present a lossless Deoxyribonucleic Acid (DNA) sequence hiding method that can be used for ensuring authenticity of DNA sequence in the context of Mobile Cloud based healthcare systems. Hiding data within DNA sequence results in permanent information loss in DNA sequence. Therefore, providing DNA sequence authenticity using data hiding is challenging. Moreover, existing works on DNA data hiding require a reference DNA sequence data to retrieve hidden data. Hence, current methods are not blind approaches and inappropriate for ensuring authenticity of DNA sequence in the Mobile Cloud. The proposed method hides authentication data within DNA sequence, extracts authentication data, and reconstructs the DNA sequence without any loss of information. From there, our proposed approach guarantees DNA sequence authenticity and integrity in Mobile Cloud based healthcare systems. We present a security analysis of our method to show that the method is secured. We conduct several experiments to demonstrate the performance of our proposed method.

Conductometric and cloud point (CP) measurement techniques have been employed in order to investigate the interaction of antibiotic drug levofloxacin hemihydrate (LFH) with anionic surfactant, sodium dodecyl sulfate (SDS) as well as a non-ionic surfactant Tween-80 (Tw-80; polyethylene sorbitol ester) in the absence/occurrence of various salts over the temperature range 298.15–318.15 K keeping regular interval of 5 K. Two clear c* values were extracted for pure SDS along with (SDS + LFH) mixtures where the increase and decrease of c* values due to the addition of LFH and salts respectively demonstrated the happened interaction among the studied components where salts provided a convenient environment for aggregation of surfactants. Negative ∆G°m values of SDS in H2O/(SDS + LFH) mixtures revealed the spontaneous assembling of surfactant molecules and the stability of the aggregates. The values of ∆H°m and ∆S°m indicate the existence of hydrophobic & electrostatic interactions between drug (LFH) and surfactant (SDS). Enthalpy and entropy contributions on the standard free energy of micellization were also calculated. The CP magnitudes of Tween-80 were reduced with increasing its concentration. The occurrence of LFH/(LFH + salts) boosted to lessening the CP values. The positive ∆G°c values suggested the non-spontaneity of clouding while the obtained ∆H°c and ∆S°c values of this CP study demonstrated the two significant accelerating impacts (electrostatic/hydrophobic interactions) between LFH & Tw-80. The thermodynamic properties of transfer for both techniques were also evaluated and analyzed in detail in the current study.

Herein, we have investigated the interaction of bovine serum albumin (BSA), the most abundant globular protein, with a conventional cationic surfactant, cetyldimethylethylammonium bromide (CDMEAB), through a conductivity technique in the absence/presence of electrolyte solutions at various temperatures (298.15–323.15 K). The interaction of the protein with drugs/surfactants and other additives plays a crucial role in the body. Hence, the main concern of the study is to extract the impact of BSA on surfactant molecules and vice versa. From the specific conductivity versus concentration of surfactant plots, three different noticeable critical micelle concentration (c*) values were obtained for pure CDMEAB and its mixture with protein/protein + salts. The presence of BSA and electrolytes altered the c* values of CDMEAB revealing interactions among the studied constituents where the salt solutions reduced the c* values and created a convenient environment for favorable micellization. The negative magnitudes achieved for standard free energy changes (ΔG0m) suggest spontaneity of micellization while the values of ΔH0m and ΔS0m signified the existence of some electrostatic and hydrophobic interactions. The values of molar heat capacity (ΔC0m) were positive as well as small which was an indication of less structural deformation. Molecular Dynamics (MD) simulation for all atoms revealed that the salt ions promoted non-covalent interaction between BSA and CDMEAB, and such interactions were not observed in the absence of the salt. Protein structure remained nearly same in spite of strong interaction with CDMEAB as evident from the overall RMSD (root-mean-square deviation) values of the alpha carbons and backbone of the protein and RMSF (root-mean-square fluctuation) values of the amino acid residues present in BSA. In this work thermodynamic parameters of transfer (such as ΔG0m.tr., ΔH0m.tr., and ΔC0p.m.tr.) were also evaluated and the results are discussed in detail. Besides, contributions of enthalpy and entropy to free energy changes were also analyzed.

We present a reversible biosignal steganography method to authenticate the source of biosignal in this paper. Cloud is being a popular platform for storing a large volume of biosignals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmogram (PPG). However, outsourcing biosignals to the cloud may introduce authenticity issues. For instance, patient data can be altered, or fake patient data can be inserted by the dishonest cloud service provider or attacker for giving benefits to business organizations such as insurance service providers. Steganography approaches can be used to hide data source's identification data before outsourcing to the cloud for maintaining authenticity. Existing biosignal steganography approaches fail to reconstruct original biosignal after applying a reverse data hiding technique. In other words, current biosignal steganography approaches are irreversible. Reversible biosignal steganography method is required for protecting biosignal data from deterioration and efficient use by its stakeholders. In this work, we develop a reversible biosignal steganography approach using the Extended Binary Golay Code based error correction method. Our proposed method embeds secret authentication message as an error within different types of biosignals such as ECG, PPG, and EEG. Extended Binary Golay Code based error correction method is used to extract the secret message, and reconstruct original biosignal. We conduct a set of experiments for evaluating the performance of our proposed method.

We present a formal model for smart contract workflow using Colored Petri-Net in the context of a blockchain-based healthcare supply chain in this paper. Ensuring traceability of products is a crucial issue in a smart healthcare supply chain. Blockchain and smart contracts are two enabling technologies that ensure the traceability of products and prevent data tampering in the smart healthcare supply chain. In a blockchain-based supply chain, a workflow of smart contracts needs to created and executed based on the input data. The selection of smart contracts in the workflow is data-driven and dynamic. Hence, it is necessary to verify the correctness of the dynamic execution of smart contracts. In this paper, we develop a Colored Petri-Net based formalism to verify the correctness of dynamic behaviors of the smart contract workflow. We conduct experiments to evaluate the performance of our proposed model. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020. authors would like to express their gratitude to the QNRF (Qatar Foundation) for its support and funding for the project activities. Scopus

A distinctive feature of polyphenolics is the possession of biological properties such as antioxidant and antimicrobial activities. Simple synthesis and study about such important class of compounds and their analogs are very important to enhance the understanding of their role in human health and diseases. Accordingly, a series of chalcones (2a-f) have been synthesized by Claisen-Schmidt condensation reaction with required acetophenones and aromatic aldehydes in high yields. The conversion of chalcones to the corresponding flavones (1a-g) taking minimal amount of dimethyl sulfoxide with iodine in presence of sulfuric acid was carried out under microwave conditions in high yields.

Over 8 million users rely on the Tor network each day to protect their anonymity online. Unfortunately, Tor has been shown to be vulnerable to the website fingerprinting attack, which allows an attacker to deduce the website a user is visiting based on patterns in their traffic. The state-of-the-art attacks leverage deep learning to achieve high classification accuracy using raw packet information. Work thus far, however, has examined only one type of media delivered over the Tor network: web pages, and mostly just home pages of sites. In this work, we instead investigate the fingerprintability of video content served over Tor. We collected a large new dataset of network traces for 50 YouTube videos of similar length. Our preliminary experiments utilizing a convolutional neural network model proposed in prior works has yielded promising classification results, achieving up to 55% accuracy. This shows the potential to unmask the individual videos that users are viewing over Tor, creating further privacy challenges to consider when defending against website fingerprinting attacks.

The website fingerprinting attack allows a low-resource attacker to compromise the privacy guarantees provided by privacy enhancing tools such as Tor. In response, researchers have proposed defenses aimed at confusing the classification tools used by attackers. As new, more powerful attacks are frequently developed, raw attack accuracy has proven inadequate as the sole metric used to evaluate these defenses. In response, two security metrics have been proposed that allow for evaluating defenses based on hand-crafted features often used in attacks. Recent state-of-the-art attacks, however, use deep learning models capable of automatically learning abstract feature representations, and thus the proposed metrics fall short once again. In this study we examine two security metrics and (1) show how these methods can be extended to evaluate deep learning-based website fingerprinting attacks, and (2) compare the security metrics and identify their shortcomings.