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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.

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

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.

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

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

Smart manufacturing systems involve a large number of interconnected devices resulting in massive data generation. Cloud computing technology has recently gained increasing attention in smart manufacturing systems for facilitating cost-effective service provisioning and massive data management. In a cloud-based manufacturing system, ensuring authorized access to the data is crucial. A cloud platform is operated under a single authority. Hence, a cloud platform is prone to a single point of failure and vulnerable to adversaries. An internal or external adversary can easily modify users' access to allow unauthorized users to access the data. This paper proposes a role-based access control to prevent modification attacks by leveraging blockchain and smart contracts in a cloud-based smart manufacturing system. The role-based access control is developed to determine users' roles and rights in smart contracts. The smart contracts are then deployed to the private blockchain network. We evaluate our solution by utilizing Ethereum private blockchain network to deploy the smart contract. The experimental results demonstrate the feasibility and evaluation of the proposed framework's performance. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. authors would like to express their gratitude to the QNRF (Qatar Foundation) for its support and funding for the project activities. 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.

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 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.

We propose the first privacy preserving search technique for stego health data in untrusted cloud in this paper. The Cloud computing is a popular technology to the healthcare providers for outsourcing health data due to flexibility and cost effectiveness. However, outsourcing health data to the cloud introduces serious privacy issues to the patient. For example, dishonest personnel of the cloud provider may disclose patient sensitive information to business organizations for some financial benefits. Using steganography, patient sensitive information is hidden within health data for privacy preservation. As a result, stego health data is generated. To the best of our knowledge, no method exists for searching a particular stego data without disclosing any information to the cloud. We propose a framework for privacy preserving search over stego health data. We systematically describe each component of the proposed framework. We conduct several experiments to evaluate the performance of the framework.

Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design and train his attack classifier based on the defense, we first demonstrate that at a straightforward technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower bandwidth overheads., Comment: 18 pages, 13 figures and 8 Tables. Accepted in IEEE Transactions on Information Forensics and Security (TIFS)

We present a privacy-preserving framework for Artificial Intelligence (AI) enabled composition for the edge networks. Edge computing is a very promising technology for provisioning realtime AI services due to low response time and network bandwidth requirements. Due to the lack of computational capabilities, an edge device alone cannot provide the complex AI services. Complex AI tasks should be divided into multiple sub-tasks and distributed among multiple edge devices for efficient service provisioning in the edge network. AI-enabled or automatic service composition is one of the essential AI tasks in the service provisioning. In edge computing-based service provisioning, service composition related tasks need to be offloaded to several edge nodes for efficient service. Edge nodes can be used for monitoring services, storing Quality-of-Service (QoS) data, and composing services to find the best composite service. Existing service composition methods use plaintext QoS data. Hence, attackers may compromise edge devices to reveal QoS data of services and modify them for giving an advantage to particular edge service providers, and the AI-based service composition becomes biased. From that point of view, a privacy-preserving framework for AI-based service composition is required for the edge networks. In our proposed framework, we introduce an AI-based composition model for edge services in the edge networks. Additionally, we present a privacy-preserving AI service composition framework to perform composition on encrypted QoS data using fully homomorphic encryption (FHE) algorithm. We conduct several experiments to evaluate the performance of our proposed privacy-preserving service composition framework using a synthetic QoS dataset.

Website Fingerprinting (WF) is a traffic analysis attack that enables an eavesdropper to infer the victim's web activity even when encrypted and even when using the Tor anonymity system. Using deep learning classifiers, the attack can reach up to 98% accuracy. Existing WF defenses are either too expensive in terms of bandwidth and latency overheads (e.g. 2-3 times as large or slow) or ineffective against the latest attacks. In this work, we explore a novel defense based on the idea of adversarial examples that have been shown to undermine machine learning classifiers in other domains. Our Adversarial Traces defense adds padding to a Tor traffic trace in a manner that reliably fools the classifier into classifying it as coming from a different site. The technique drops the accuracy of the state-of-the-art attack from 98% to 60%, while incurring a reasonable 47% bandwidth overhead, showing its promise as a possible defense for Tor.

Water stress, by affecting the rate of grain filling, decreased yield per panicle of rice by 29–40% in the small-seeded variety IR747(L) and by 19–32% in the large-seeded variety PP6R-13-12-3. It had little effect on the duration of grain filling. High nitrogen level increased culm+sheath and leaf blade weights substantially in both varieties, but decreased starch concentrations (determined for IR747(L) only) at flowering in the culm+sheath significantly. Relatively more starch remained in the culm+sheath of the water-stressed plants at maturity. The balance sheet suggested that plants grown at a low nitrogen level were more dependent on “reserve starch” and that the percentage of reserve starch that went to the panicle was higher in the stressed plants than in the unstressed plants. High nitrogen level increased the nitrogen concentration in the culm+sheath at flowering by about 80%. At harvest, the concentration decreased substantially irrespective of nitrogen levels and varieties. Relatively more nitro...

Paddy harvesting is labor and time intensive operation. There is an acute shortage of labor in pick paddy harvesting seasons in Bangladesh that promotes the growing market potential for providing hiring services of reaper by small entrepreneur to the farmers. Therefore, the study was conducted to determine the economic performance of Custom Hiring Service (CHS) of reaper for harvesting paddy in Bangladesh. Primary data were collected through field experiments and survey at Dumuria, Wazirpur, Subarnachar and Kalapara Upazilas of Khulna, Barisal, Noakhali and Patuakhali districts, respectively, of Bangladesh during six paddy harvesting seasons. Economic analyses for CHS of reaper were also carried out in the study. Operating costs were calculated and financial profitability was determined by four major financial parameters, namely, benefit-cost ratio (BCR), net present value (NPV), internal rate of return (IRR) and payback period. The BCR of the reaper was found 2.04 which indicate that the reaper is highly profitable for the hiring service. Considering 10% discount rate, NPV of the reaper was found BDT.426106 which indicates that reaper can be considered as financially viable. The estimated IRR of the reaper was found 91%, which is far greater than the bank rate of interest (9%). The payback period of the reaper was found 1.06 years. The break-even use of the reaper was found 14.79 ha per year. Major indicators in this analysis show that the investment on a reaper is highly profitable for custom hiring service. This analysis also supports for growing custom hiring service market of reaper in rural Bangladesh. [J Bangladesh Agril Univ 2020; 18(5.000): 872-879]

We propose a smart contract workflow verification framework for blockchain-based supply chain management systems. The proposed framework introduces a Petri-Net-based formalism to model smart contract workflow in a supply-chain context. Smart contracts are deployed in the blockchain nodes and executed automatically if a predefined condition is met. As deployment and execution of smart contracts require payment, it is necessary to ensure smart contract logic before the deployment to avoid unnecessary execution costs. Multiple business rules should be followed in a predefined order and criteria to complete a complex supply chain process. Therefore, a set of smart contracts representing digital business rules should also be executed in a predefined order and criteria. To verify the soundness of smart contract-based blockchain systems, the modeling of smart contract execution is required. The proposed Petri-Net model for smart contracts ensures smart contract workflow correctness before execution in a blockchain-based system. We conduct multiple experiments to evaluate the performance of our proposed framework. 2021 IEEE Computer Society. All rights reserved. This work is part of the NPRP11S-1227-170135 project. The authors would like to express their gratitude to the QNRF (Qatar Foundation) for its support and funding for the project activities. Scopus