Hyperledger fabric

We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.

Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates according to their significance. Differential privacy and secure update protocols safeguard data while preserving accuracy. The A3-FL framework is evaluated in this study using FVC2004 fingerprint data, with each client's features extracted using a Siamese Convolutional Neural Network (Siamese-CNN). By dynamically modifying client contributions, the attention mechanism increases the accuracy of the global model.The accuracy, convergence speed, and robustness of the A3-FL framework are superior to those of standard FL (FedAvg) and static baselines, according to experimental evaluations using fingerprint data (FVC2004). The accuracy of the attention-based approach was 0.8413, while FedAvg, Local-only, and Centralized approaches were 0.8164, 0.7664, and 0.7997, respectively. Accuracy stayed high at 0.8330 even with differential privacy. A scalable and privacy-sensitive biometric system for secure and effective recognition in dispersed environments is presented in this work.

Traditional ride-sharing platforms rely on centralized architectures, leading to concerns about data privacy, transparency, and security vulnerabilities. These issues, coupled with high service fees and susceptibility to cyber threats, highlight the need for a decentralized, privacy-preserving alternative. To address these challenges, we propose a semi-public blockchainbased ride-sharing platform that leverages Hyperledger Fabric for secure, permissioned data storage and IPFS for distributed data management. Our system integrates Ethereum smart contracts for transaction transparency and incorporates the Cosmos SDK to enable seamless interoperability between private and public blockchains. Additionally, we introduce a fair "pay-as-youdrive" mechanism, replacing conventional time-locked deposit protocols to ensure users only pay for the distance traveled. By combining permissioned blockchain security with decentralized storage and efficient cross-chain communication, our approach ensures privacy, scalability, and real-world applicability. Our performance analysis demonstrate that our Proof-of-Stake consensus mechanism achieves 15-20 percent higher throughput and 30-40 percent lower latency compared to PoW and PoA, alongside reduced CPU usage under high transaction loads. With a monthly operational cost of approximately 30,000 BDT per node, the platform proves both affordable and scalable, offering a practical path toward decentralized, secure ride-sharing solutions.

Ride-sharing platforms have revolutionized urban mobility, offering millions of users convenient and costeffective transportation. However, mainstream centralized platforms such as Uber and Lyft continue to face pressing concerns including data privacy breaches, high service charges, security vulnerabilities, and a lack of transparency due to centralized control. To address these limitations, this research proposes a semipublic blockchain-based ride-sharing platform integrating Hyperledger Fabric for secure and permissioned data management with Ethereum smart contracts for transparent ride booking, fare calculation, and payments. The platform leverages the InterPlanetary File System (IPFS) for immutable, decentralized storage and uses the Cosmos SDK to enable seamless interoperability between public and private blockchains. A user-centric pay-as-you-drive model is introduced to ensure fair and distance-based billing. Preliminary evaluations show that our system outperforms traditional blockchain consensus methods (PoW, PoA) in throughput, latency, and resource usage. At the same time, it remains economically viable with an operational cost of under 33,000 BDT per node. Future improvements include benchmarking with Hyperledger Caliper, transitioning from Vagrant to Docker for better scalability, and implementing backend services using Node.js or Golang with MongoDB for efficient metadata handling. Together, these enhancements support a secure, decentralized, and scalable alternative to existing ride-sharing systems.

Physics plays a fundamental role in shaping modern industries by providing the theoretical framework and practical tools necessary for innovation and progress. This abstract provides an overview of the practical applications of physics in various industries, highlighting its contributions to technology, energy, manufacturing, healthcare, and beyond. Physics-based advancements have led to the development of cutting-edge products, improved processes, and enhanced safety standards, thereby driving economic growth and improving the quality of life. Physics is at the heart of the electronics industry, enabling the creation of transistors, semiconductors, and microprocessors. These advancements underpin the development of computers, smart phones, and other electronic devices, making them faster, smaller, and more energyefficient. The practical application of physics in industries is of significant importance for several reasons. Physics is the fundamental science that provides the underlying principles and understanding of how the physical world works. When applied to various industrial sectors, it leads to innovation, efficiency, and improved products and processes. Here are some key research significances and practical applications of physics in industries. Enhanced Product Development: Physics plays a crucial role in the development of new products, especially in high-tech industries like electronics and aerospace. Research in materials science, thermodynamics, and quantum physics, for instance, contributes to the creation of more efficient and durable materials and components. In the weighted amount approach, each of our goals by the weight provided by the user multiply our goals the package is the only goal we measure. This time very widely used one of the approaches. Weighted amount approaches mind that comes to mind when done the question is, for every goal what weights should be assigned doing the same. Multi-departural optimization in trouble, we have many objectives there are functions; they are we want to increase or reduce. Many obstacles to our problem there are, they are our potential to complement any solutions should. These issues global parato package we are trying to find out. Global parato-approved package, often pareto-flexible referred to as package, points that do not have other points package, then these points and yet our potential best in the region. From the result Alternative E got first rank whereas alternative D has lowest rank.

Blockchain technology has emerged as a disruptive innovation offering transparency, immutability, and decentralized trust. These characteristics make blockchain highly valuable in applications such as finance, supply chain, healthcare, and e-governance. However, the same transparency that strengthens trust often conflicts with the principles of data privacy and the protection of individual rights. Regulations such as the European Union's General Data Protection Regulation (GDPR) and Indonesia's Personal Data Protection (PDP) Law emphasize individual control over personal data, including the right to be forgotten, which contradicts blockchain's immutable nature. This paper explores the dilemma between transparency and privacy in blockchain systems by analyzing key challenges, including the exposure of sensitive data, difficulties in regulatory compliance, and ethical concerns. Through a review of relevant literature, case studies, and regulatory frameworks, this study highlights the tension between openness and confidentiality. Furthermore, the paper discusses potential technical and regulatory solutions such as zero-knowledge proofs, off-chain storage, permissioned blockchains, and adaptive legal frameworks. The findings suggest that resolving the transparency-privacy dilemma requires a balanced approach that integrates technological innovation with responsive governance to ensure both trust and individual rights are preserved in blockchain-based ecosystems.

The Internet of Vehicles (IoV) enhances road safety through real-time vehicle-to-vehicle (V2V) communication of traffic messages. However, V2V wireless connectivity poses security and privacy threats, as malicious adversaries can eavesdrop and modify V2V messages or compromise vehicle identity privacy. Existing authenticated key agreement (AKA) schemes attempt to address these threats but suffer from security flaws, computational inefficiency, high communication overhead, single points of failure, and trust deficits, making them unsuitable for resource-constrained and delay-sensitive IoV applications. To address the above challenges, we propose a blockchain-powered AKA scheme with a reputation-incentive mechanism (BAKARI) for V2V communication. BAKARI employs Schnorr signatures and lightweight elliptic curve cryptographic operations to improve computational efficiency, and minimizes communication overhead by completing the AKA phase with only two messages. BAKARI leverages blockchain ledger and smart contracts to maintain vehicle authentication information and V2V messages. Additionally, it incorporates a reputation-incentive model, where trustworthy vehicles are rewarded while malicious ones are penalized. A rigorous security analysis, including formal proof under the random or real model, informal analysis, and ProVerif verification, demonstrates BAKARI's resilience against security and privacy threats. Performance evaluation shows that BAKARI balances computational efficiency, communication overhead, and security better than the benchmark schemes. Finally, simulations on Hyperledger Fabric and Veins frameworks validate BAKARI's practicality in real-world IoV environments.

Global supply chains suffer from fragmented data silos, limited transparency, and vulnerability to fraud/counterfeiting. Traditional centralized systems fail to provide real-time, immutable traceability, leading to inefficiencies in recalls, compliance, and stakeholder trust. We propose blockchain-based architecture leveraging distributed ledger technology (DLT) and smart contracts to create an end-to-end transparent, tamper-proof traceability system. This work designs and validates an enterprise-ready blockchain framework (Hyperledger Fabric) integrated with IoT sensors, uniquely addressing scalability and interoperability gaps in prior solutions. It quantifies performance-security trade-offs and stakeholder adoption barriers. A modular architecture was implemented, combining RFID/GPS sensors for data acquisition, PBFT consensus, and automated smart contracts. A real-world agri-food supply chain case study (organic coffee) evaluated performance, security, and usability across 5 stakeholder tiers. The system achieved 350 TPS throughput with <2-second latency, reducing paperwork by 85% and dispute resolution time by 30%. Security audits confirmed zero tampering incidents. Stakeholder surveys (N=42) showed 89% trust improvement but highlighted cost (72%) and technical literacy (58%) as adoption hurdles. Comparative analysis demonstrated 40% lower operational redundancy versus hybrid systems. Blockchain significantly enhances supply chain transparency and traceability with measurable efficiency gains. Future work will integrate AI-driven predictive analytics and cross-chain protocols.

This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra, and OpenFL. The frameworks are evaluated experimentally by implementing federated learning over a varying number of clients, emphasizing a thorough analysis of scalability and key performance metrics. The study assesses the impact of increasing client counts on total training time, loss and accuracy values, and CPU and RAM usage. Results indicate distinct performance characteristics among the frameworks, with Flower displaying an unusually high loss, FedML achieving a notably low accuracy range of 66% to 79%, and Substra demonstrating good resource efficiency, albeit with an exponential growth in total training time. Notably, OpenFL emerges as the most scalable platform, demonstrating consistent accuracy, loss, and training time across different client counts. OpenFL's stable CPU and RAM underscore its reliability in real-world scenarios. This comprehensive analysis provides valuable insights into the relative performance of FL frameworks, offering good understanding of their capabilities and providing guidance for their effective deployment across diverse user bases.

Although the Office of The National Coordinator for Health Information Technology's (ONC) Information Blocking Provision in the Cures Act Final Rule is an important step forward in providing patients free and unfettered access to their electronic health information (EHI), in the contexts of multiuser electronic health record (EHR) access and proxy access, concerns on the potential for harm in adolescent care contexts exist. We describe how the provision could erode patients' (both adolescent and older patients alike) trust and willingness to seek care. The rule's preventing harm exception does not apply to situations where the patient is a minor and the health care provider wishes to restrict a parent's or guardian's access to the minor's EHI to avoid violating the minor's confidentiality and potentially harming patient-clinician trust. This may violate previously developed government principles in the design and implementation of EHRs for pediatric care. Creating legally acceptable workarounds by means such as duplicate "shadow charting" will be burdensome (and prohibitive) for health care providers. Under the privacy exception, patients have the opportunity to request information to not be shared; however, depending on institutional practices, providers and patients may have limited awareness of this exception. Notably, the privacy exception states that providers cannot "improperly encourage or induce a patient's request to block information." Fearing being found in violation of the information blocking provisions, providers may feel that they are unable to guide patients navigating the release of their EHI in the multiuser or proxy access setting. ONC should provide more detailed guidance on their website and targeted outreach to providers and their specialty organizations that care for adolescents and other individuals affected by the Cures Act, and researchers should carefully monitor charting habits in these multiuser or proxy access situations.

Although the Office of The National Coordinator for Health Information Technology's (ONC) Information Blocking Provision in the Cures Act Final Rule is an important step forward in providing patients free and unfettered access to their electronic health information (EHI), in the contexts of multiuser electronic health record (EHR) access and proxy access, concerns on the potential for harm in adolescent care contexts exist. We describe how the provision could erode patients' (both adolescent and older patients alike) trust and willingness to seek care. The rule's preventing harm exception does not apply to situations where the patient is a minor and the health care provider wishes to restrict a parent's or guardian's access to the minor's EHI to avoid violating the minor's confidentiality and potentially harming patient-clinician trust. This may violate previously developed government principles in the design and implementation of EHRs for pediatric care. Creating legally acceptable workarounds by means such as duplicate "shadow charting" will be burdensome (and prohibitive) for health care providers. Under the privacy exception, patients have the opportunity to request information to not be shared; however, depending on institutional practices, providers and patients may have limited awareness of this exception. Notably, the privacy exception states that providers cannot "improperly encourage or induce a patient's request to block information." Fearing being found in violation of the information blocking provisions, providers may feel that they are unable to guide patients navigating the release of their EHI in the multiuser or proxy access setting. ONC should provide more detailed guidance on their website and targeted outreach to providers and their specialty organizations that care for adolescents and other individuals affected by the Cures Act, and researchers should carefully monitor charting habits in these multiuser or proxy access situations.

Resumen-Desde hace a ños, la seguridad en las redes P2P estructuradas está siendo cuestionada, y por ello se han propuesto muchos trabajos con el objetivo de proporcionar enrutamiento seguro, sistemas de reputación, control de acceso, confidencialidad de los datos, etc. Sin embargo, el proceso de asignación de identidades se ha dejado casi totalmente olvidado. Estas redes están dise ñadas para que cada usuario tenga un identificador único (nodeID), pero la mayoría de los sistemas existentes permiten que los usuarios puedan obtener un conjunto de ellos, e incluso seleccionarlos. Ambas actuaciones provocan problemas importantes de seguridad, ya que gracias a ello los usuarios pueden alterar el adecuado funcionamiento de la red. En este trabajo proponemos un protocolo de asignación de nodeIDs basado en la emisión de certificados implícitos. Nuestro propósito es proporcionar servicios de seguridad que permitan luchar contra la mayoría de las amenazas que sufren estas redes, con especial atención a la asignación de identidades. Este protocolo se basa en el uso de certificados y la generación conjunta de nodeIDs por parte la Autoridad de Certificación (CA) y el nuevo usuario.

Numerous applications have been developed using Blockchain in recent years as its popularity has grown. Any application that uses Blockchain technology as part of its architectural model guarantees the integrity of its data. Using a decentralized Blockchain system to verify drugs, companies, and brands can prevent counterfeiting while reducing costs at the same time. This paper presents a secure Blockchain-based model to counterfeit fake products in the market. A key is generated for each product, which is stored in the Blockchain. This key will also be fed into a QR code which will be attached to each product. This model uses a QR code-based approach to verify the truthfulness of the product by searching the Blockchain. As the Blockchain is immutable in nature, the data cannot be breached or modified. Also designed is an accompanying web-based application to implement this approach and discusses the software implementation process where the application scans the product code and confirms whether the given product is fake or not.

Numerous applications have been developed using Blockchain in recent years as its popularity has grown. Any application that uses Blockchain technology as part of its architectural model guarantees the integrity of its data. Using a decentralized Blockchain system to verify drugs, companies, and brands can prevent counterfeiting while reducing costs at the same time. This paper presents a secure Blockchain-based model to counterfeit fake products in the market. A key is generated for each product, which is stored in the Blockchain. This key will also be fed into a QR code which will be attached to each product. This model uses a QR code-based approach to verify the truthfulness of the product by searching the Blockchain. As the Blockchain is immutable in nature, the data cannot be breached or modified. Also designed is an accompanying web-based application to implement this approach and discusses the software implementation process where the application scans the product code and confirms whether the given product is fake or not.

For businesses looking to take advantage of cloud service, choosing the best cloud service provider is essential. It is necessary to take into account variables like scalability, reliability, security, and cost-effectiveness. It is crucial to evaluate the provider's infrastructure, service level agreements, support choices, and interoperability with existing systems in order to make a well-informed choice that satisfies particular business demands. The impact that cloud service selection has on enterprises and organizations is what gives the topic study significance. Making informed choices about cloud service providers can have several advantages, including cost reduction, increased scalability, greater data protection, and simpler business processes. Organizations can choose cloud service providers that meet their unique needs by performing research on the topic. This results in more effective resource allocation, more productivity, and better overall performance. Additionally, research in this field assists in identifying new trends, best practices, and potential hazards related to the use of cloud services, assisting in the creation of frameworks and strategies for informed decision-making Weighted sum method is a decision-making technique that assigns weights to various criteria and calculates a weighted sum to determine the best alternative based on those criteria.

A Cloud-Integrated Framework for Efficient Government Financial Management and Unclaimed Asset Recovery abstract. The allocation and usage of government resources have always been a concern for citizens and practitioners. Over the years, various tools and frameworks have been developed to facilitate good government resource management. More recently governments have acquired and created data and information that can be used to analyze spending patterns and reveal hidden treasures such as unclaimed properties, abandoned warehouse sales, and unused
accounts, which helps with efficient resource allocation. Additionally, governments can make use of new technologies, including data mining, data management, and cloud computing, to manage and monetize their data pool. This paper presents a framework that integrates various technologies and methods such as a data warehouse, cloud computing,
interactive visualization, and data mining techniques to develop an information system that can be implemented by governments to facilitate financial management. Efficient and effective government financial management can direct resources toward projects that support growth and create welfare for citizens. In a world where every activity generates
a digital asset, governments have access to and already possess a pool of data that offer opportunity to make more effective government decisions. This paper presents a cloud-integrated framework that combines new data management technologies, including a government cloud, interactive visualization, and data mining techniques, and creates an
effective set of services, and presents a solution to the problem of inefficient government financial management. The proposed framework is applied to assist governments in monetary allocation decisions and provides a decision support system for both citizens and government decision makers. Through the proposed framework, governments can track where funds are employed within their organization and compare borrowed funds with allocated budgets, thereby enabling independent researchers to analyze how effectively specific funds are employed. This leads to the proposal of a service that could be designed on top of the framework to do unclaimed property analysis for citizens.

Digital insurance services already provide consumers many benefits; however, they bring multiple risks as well. Privacy and personal data protection risks, attacks to the cybersecurity model, fraud, misinformation, among others, threaten these consumer benefits in a direct way. The growing challenge for policy makers is to guarantee data protection and control for consumers while not undermining the role of other stakeholders such as capital markets and financial institutions. Although the limited time frame for the implementation of the relevant regulations is a huge challenge for the relevant authorities, it should not lead to generic solutions, which without knowledge of the specific context and of the opposing stakeholder interests will not work appropriately. Moreover, they should not lead to models which cannot be implemented easily and quickly in practice. The design of transparent and resilient digital identification frameworks should take place after careful examination of the public and private ecosystems and of their intrinsic characteristics, in close cooperation with societal stakeholders, and focusing on proper incentive schemes. Insurance companies introduced improved scalability and resilience in their digital identity protection approaches, but at the resulting higher costs. The policy makers now face the challenge to improve the protection of personal data and identifications on the cloud infrastructure of the insurance companies. They should remain well aware of the fact however, that quick and easy to obey regulation seems impossible. For example, decentralized identity platforms are at an early stage. Consequently, their universal implementation cannot be quick, as many stakeholders should be engaged to successfully operate through decentralized identities. On the other hand, promoting some characteristics of decentralized identity solutions through standards (or at least usable solutions with such characteristics) could be done as an initial step in a quick and effective way. Finally, understanding of context and incentives for compliance should be introduced in the design of regulations at the outset. Privacy preserving solutions that protect the consumers data and identifications are not in the best financial interests of the insurance companies as there are important sunk costs in their current digital identity and personal data protection infrastructure. Understandably, their desire for a quick windfall gain in compliance will lead to a generic regulation finally hurting consumers rights.

Digital insurance services already provide consumers many benefits; however, they bring multiple risks as well. Privacy and personal data protection risks, attacks to the cybersecurity model, fraud, misinformation, among others, threaten these consumer benefits in a direct way. The growing challenge for policy makers is to guarantee data protection and control for consumers while not undermining the role of other stakeholders such as capital markets and financial institutions. Although the limited time frame for the implementation of the relevant regulations is a huge challenge for the relevant authorities, it should not lead to generic solutions, which without knowledge of the specific context and of the opposing stakeholder interests will not work appropriately. Moreover, they should not lead to models which cannot be implemented easily and quickly in practice. The design of transparent and resilient digital identification frameworks should take place after careful examination of the public and private ecosystems and of their intrinsic characteristics, in close cooperation with societal stakeholders, and focusing on proper incentive schemes. Insurance companies introduced improved scalability and resilience in their digital identity protection approaches, but at the resulting higher costs. The policy makers now face the challenge to improve the protection of personal data and identifications on the cloud infrastructure of the insurance companies. They should remain well aware of the fact however, that quick and easy to obey regulation seems impossible. For example, decentralized identity platforms are at an early stage. Consequently, their universal implementation cannot be quick, as many stakeholders should be engaged to successfully operate through decentralized identities. On the other hand, promoting some characteristics of decentralized identity solutions through standards (or at least usable solutions with such characteristics) could be done as an initial step in a quick and effective way. Finally, understanding of context and incentives for compliance should be introduced in the design of regulations at the outset. Privacy preserving solutions that protect the consumers data and identifications are not in the best financial interests of the insurance companies as there are important sunk costs in their current digital identity and personal data protection infrastructure. Understandably, their desire for a quick windfall gain in compliance will lead to a generic regulation finally hurting consumers rights.

Digital insurance services already provide consumers many benefits; however, they bring multiple risks as well. Privacy and personal data protection risks, attacks to the cybersecurity model, fraud, misinformation, among others, threaten these consumer benefits in a direct way. The growing challenge for policy makers is to guarantee data protection and control for consumers while not undermining the role of other stakeholders such as capital markets and financial institutions. Although the limited time frame for the implementation of the relevant regulations is a huge challenge for the relevant authorities, it should not lead to generic solutions, which without knowledge of the specific context and of the opposing stakeholder interests will not work appropriately. Moreover, they should not lead to models which cannot be implemented easily and quickly in practice. The design of transparent and resilient digital identification frameworks should take place after careful examination of the public and private ecosystems and of their intrinsic characteristics, in close cooperation with societal stakeholders, and focusing on proper incentive schemes. Insurance companies introduced improved scalability and resilience in their digital identity protection approaches, but at the resulting higher costs. The policy makers now face the challenge to improve the protection of personal data and identifications on the cloud infrastructure of the insurance companies. They should remain well aware of the fact however, that quick and easy to obey regulation seems impossible. For example, decentralized identity platforms are at an early stage. Consequently, their universal implementation cannot be quick, as many stakeholders should be engaged to successfully operate through decentralized identities. On the other hand, promoting some characteristics of decentralized identity solutions through standards (or at least usable solutions with such characteristics) could be done as an initial step in a quick and effective way. Finally, understanding of context and incentives for compliance should be introduced in the design of regulations at the outset. Privacy preserving solutions that protect the consumers data and identifications are not in the best financial interests of the insurance companies as there are important sunk costs in their current digital identity and personal data protection infrastructure. Understandably, their desire for a quick windfall gain in compliance will lead to a generic regulation finally hurting consumers rights.

The banking sector is increasingly leaning on advanced technologies to optimize infrastructure services, particularly through the deployment of Artificial Intelligence (AI) and machine learning methodologies. This movement is driven by the need for enhanced decision-making capabilities, improved operational efficiencies, and robust risk management strategies. Federated Learning emerges as a pivotal framework within this context, allowing institutions to collaboratively train AI models without compromising sensitive customer data. This decentralized approach not only mitigates privacy risks, but also enriches the training datasets, ultimately yielding more accurate and reliable predictive models. Such models are crucial for applications that encompass fraud detection, customer segmentation, and algorithmic trading. Furthermore, the implementation of AI governance frameworks is instrumental in navigating the ethical and regulatory complexities that accompany the integration of AI in banking. Effective governance ensures that AI systems operate transparently, with accountability mechanisms in place to address potential biases and ethical dilemmas. This is particularly important in a sector that manages vast amounts of personal and financial information. By instituting comprehensive oversight policies, banks can foster trust among stakeholders while enhancing compliance with regulatory requirements. The synergy between Federated Learning and AI governance thus not only fortifies the technological backbone of banking IT but also aligns operational practices with ethical standards and consumer protection mandates. This interplay between collaborative AI initiatives and stringent governance encapsulates the future of banking infrastructure. As institutions embrace these innovative solutions, they position themselves to harness the full potential of data-driven insights while safeguarding customer interests. Consequently, the dual focus on optimizing infrastructure services through Federated Learning and enforcing AI governance emerges as a key strategic approach within the banking landscape. This comprehensive framework is essential for navigating the complexities of a rapidly evolving financial ecosystem, ultimately facilitating sustainable growth and enhancing competitive advantage in the marketplace.

The integration of data analytics, artificial intelligence (AI), and cloud computing has significantly transformed the healthcare industry, particularly in optimizing Medicaid and enhancing patient outcomes. This paper delves into the latest advancements in predictive analytics, AI-driven diagnostics, blockchain integration, and cloud-based healthcare solutions aimed at improving efficiency. Through a comprehensive literature review, this study examines the role of AI and data-driven decision-making in enhancing data quality, interoperability, and workforce training. The findings highlight emerging trends, challenges, and future directions, paving the way for continued research and innovation in healthcare technology.

Blockchain technology is a revolutionary innovation with immense disruptive potential and can change many sectors. Health care is one of the sectors that will greatly benefit from using this technology, with critical requirements for secure, transparent, and efficient data systems. Decentralization and blockchain's tamper evident structure make patient confidential information more resilient against unauthorized access and data manipulation. Blockchain also facilitates the improvement of interoperability between health care providers, data sharing, and clinical processes. This paper will describe how blockchain addresses patient data security issues, fraud prevention, and compliance with regulatory standards. It further talks about the role of IT managers in adopting blockchain through strategic planning, team training, and robust security measures to enable seamless integration and optimal functionality.

Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient&#39;s healthcare data, learning-based systems in healthcare can greatly benefit from privacy-preserving FL approaches. However, typical model aggregation methods in FL are sensitive to local model updates, which may lead to failure in learning a robust and accurate global model. In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data. Furthermore, we show that such methods can detect and discard faulty or malicious local clients during training. We run two sets of experiments using two real-world healthcare datasets for training medical diagnosis classification tasks. Each dataset is used to simulate the performance of three different robust FL aggregation strategies when facing different poisoning attacks. The results show that privacy preserving methods can be...

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by meas...

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also provid...

For the welfare of society and many working sectors, people keep working on improving the scope and features of technology to make tasks easier. Many of these profound technology helps law enforcements and corporates to protect their data privacy but in contrast, those are also being used to steal sensitive data and do various kinds of cybercrime. Cybercrimes involves usage of both coding and software tools to find vulnerabilities to crack a system but with the creation and advancement of Artificial Intelligence, such tasks become so much easier. AI also paved the way to make new updates and features in the cyber space. Foundation of Artificial Intelligence was laid with research in fields of brain, computation and electrical networks which made scientists to work on creating a computer that is capable of thinking on its own 2. Geoffrey Hinton, the godfather of AI himself has that use of AI should be tread carefully 14. Social media acts as a medium of large amount of photographic, video and audio content. These data can be used to create, edit and morph images, videos and audios with minimal effort. Propagation of a websites that has AI features are being used to morph images of person to past it in a pornographic content. Deepart.io is one of the websites that got banned because it allowed creation of morphed pornographic content 1. Regulations and restrictive guidelines on social media for posting images are weak in some popular applications like telegram and twitter. It allows such morphed pornographic content to propagate in cyber space easily. It's nearly impossible to find difference between a normal image and morphed image without use of technical softwares which is a primary cause for increasing victims of this cybercrime. Government organizations doesn't provide enough awareness on this topic among people either. The simply and proper way to provide awareness and avoid these crimes is within the creation of the problem itself, which is the use of Artificial Intelligence itself.

Blockchain technology is a digital decentralized data ledger recording transactions in an encrypted format. Its implementation can potentially hold significant advantages for the built environment, particularly in manufacturing and building product usage aligned with Building Information Modeling (BIM). This paradigm shift toward decentralized transactions can foster security, reliability, and accountability. Hyperledger Fabric (HLF), an enterprise-grade distributed ledger, offers a modular, scalable, and confidential digital framework. This article introduces HLF-based workflows to address inefficiencies in BIM and fastener product lifecycle management, such as fragmented data handling and limited process automation. Leveraging chaincodes linked to BIM models, HLF simplifies, enhances transparency, and automates construction product lifecycle processes. Contract data and execution details are managed through a blockchain stored in a common data environment (CDE) and linked to chaincodes. The article presents the conceptualization and implementation of automated workflows, emphasizing efficiency and transparency. While showcasing successful deployments, it also highlights areas for future improvement and development. The proposed framework represents a pioneering step toward a decentralized cooperative environment in the construction industry, aligning with the transformative potential of blockchain technology.

Blockchain innovation offers an information structure with built-in safeguards, including agreement, decentralization, and encryption, which ensure the accuracy of operations. It has broad use in a variety of fields, including smart factories, the Internet of Things (IoT), and healthcare. It is particularly relevant in the areas of healthcare information safety and privacy preservation. Digital healthcare records have been deployed faster because of communication technology, however, this has also increased risks to patient confidentiality, safety, and medical information. Another strategy to deal with the issue of medical data confidentiality and safety is the application of blockchain. Medical and health information includes treatment data gathered during patient care alongside private prevention of illness. Blockchain systems can be used in numerous capacities in healthcare organizations, including confidentiality and safety, to shield information about patients from unwanted access. Nevertheless, healthcare networks confront numerous security concerns, including connectivity, reliability, exchange of clinical information delivery, and patient deliberation, as a result of the inexperienced design of safety measures. Furthermore, deployment and information administration are the main issues for blockchain in healthcare due to the enormous amount of manufactured hardware gadgets. The platform is a communication mechanism that combines the use of computers with health records. It has been suggested that Hyperledger is the most developed collaborative chain technology. In contrast with various blockchain platforms, Hyperledger concentrates on developing enterprise-level standardized implementations. The Hyperledger framework is used for sensitive data in digital welfare, but neither restricted access nor thorough permission was taken into account. It is simple to determine fraudulent data collection by a malevolent person via confirmation of data by additional subjects, and the original, unmodified information can be recovered. Data can only be influenced if a bad person has access to over 50% of the blockchain network's nodes through security breaches, which is nearly unattainable. As a result, blockchain technology can stop information from being faked or falsified, improving the data's durability and dependability.

In today's interconnected IoT ecosystems, blockchain technology acts as a facilitator for decentralization. Hyperledger Fabric (HLF) is a renounced permissioned blockchain platform designed for enterprise use. Currently, HLF supports two primary consensus algorithms: Raft and smartBFT. Despite HLF's modular nature, integrating new consensus algorithms remains a challenging and intricate process that lacks sufficient guidelines. In this work, we bridge this gap by providing a novel, comprehensive, yet practical guide to simplify the integration of consensus algorithms into HLF. Furthermore, we demonstrate our approach by integrating a new hybrid algorithm into HLF, specifically tailored for the IoT ecosystem. Compared to the current algorithms, experiments indicate promising performance and reliability.

Healthcare systems globally are transforming through technological innovations such as Artificial Intelligence (AI) and blockchain. Medicaid, which supports millions of low-income individuals in the United States, stands at the cusp of benefitting from these advancements. This paper delves into how AI improves Medicaid services through predictive analytics, care personalization, and cost control while blockchain secures data and enhances interoperability. Additionally, the study emphasizes responsible AI principles to ensure equitable and ethical application of these technologies. Insights are drawn from established frameworks and recent studies to highlight the transformative potential of AI and blockchain in Medicaid optimization

In an age of ongoing machine learning and deep learning applications, energy-efficient AI clusters are valuablein that they greatly reduce carbon footprints. AI clusters have become indispensable for large models that have a longtraining time and require large amounts of data. AI clusters can be divided into two major parts for their operation: the first part is to train the model in the deep learning model. The second part is to store the massive amount of high dimensional input data for our model. Using AI clusters in the deployment of cloud infrastructures, as well as high-speed storage solutions, has been integrated. Given the large computational costs of large-scale AI jobs, it is logical to optimize energy resources for both aspects individually. Little research has been done, however, on the relationship between storage and CPU energy optimization. Modern state-of-the-art AI systems mainly depend on the assignment of CPU-bound, disk-bound, or GPU-bound parts, connected over network links. While the usage of some of these elements can be diminished, usually the entire connection is cut off along the device chain, resulting in rapid degradation of the performance of the overall AI application. Energyefficient and environmentally friendly technical implementation is highly significant. Artificial Intelligence is evolving rapidly, providing excellent solutions for many new challenges as well as making existing solutions even better. However, one of the main challenges is the enormous consumption of energy in the training process of AI

This research paper presents a comprehensive analysis of Azure Migrate's capabilities and methodologies for physical-to-cloud migration. Through extensive examination of technical architectures, migration strategies, and performance optimization techniques, we provide insights into effective migration practices while considering security, compliance, and operational excellence. The study incorporates real-world implementation data, performance metrics, and best practices gathered from enterprise-scale migrations conducted between 2022 and 2024.

A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK.

Block chain technology has surfaced as a transformative influence, revolutionizing the verification and recording of transactions across industries. In to traditional data base systems, presenting a novel approach to database management. The objective is to enhance the trust, transparency, and security of data storage and transactions. Research in this field aims to strengthen data protection, reduce the risk of unauthorized access, and prevent data tampering. By providing a transparent and auditable record of transactions, block chain enhances trust among parties. This is particularly important in industries such as finance, where the integrity of transactions is paramount. The Weighted Sum Model (WSM) stands as a straightforward and widely utilized method of multi-criteria decision-making. It operates by multiplying assigned value of each attribute by the weight of importance designated by the decision maker. Subsequently, it entails aggregating these products across all criteria to compute evaluation scores for each alternative parameters considered in this study include Bitcoin, Ethereum, Biance Smart Chain, Hyperledger Fabric, and Corda, evaluated based on criteria such as Transaction Throughput (tps), Security Level (1-10), Energy Efficiency (kWh/tx), and Initial Setup Cost (USD). the term "Weighted Sum Model (WSM)" is correctly spelled, but the term "multi-Criteria" should be "multi-criteria" for proper phrasing. It involves assigning a value to each attribute, determined by the decision maker, which is then multiplied by a specified importance weight Bitcoin, Ethereum, Binance Smart Chain, Hyperledger Fabric and Corda. Transaction Throughput (tps), Security Level (1-10) Energy, Efficiency (kWh/tx) and Initial Setup Cost (USD). the ranking for blockchain-based database systems. Binance Smart Chain) got the top rank, whereas faculty strength Bitcoin) has a low ranking.

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by meas...

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also provid...

Mental health is an important aspect of well-being as it encompasses emotional, psychological and social well-being. The use of patient portals in mental health care has gained attention as a potential tool to improve access to care for individuals with mental illness. Patient portals may be vulnerable to unauthorized access if appropriate security measures are not put in place. This study leverages blockchain technology to create tamper-proof patient records. The proposed solution uses an on-chain database that stores hashes and the actual medical record of a patient as well as an off-chain solution that handles encryption of each user's medical record using their respective keys in a trustless manner before they are uploaded on-chain. A secure smart contract hosted on Ethereum and the Byzantine Fault Tolerance consensus algorithm was used to ensure patient privacy. The research employed the Comparative Analysis Research Methodology as the research methodology and the Kanban methodology as the software development methodology. The research project concludes that the proposed solution addresses the current security issues and data privacy concerns in patient data. The decentralized nature of blockchain ensures security, transparency, and tamper-Proceedings of the International Conference on Industrial Engineering and Operations Management ©IEOM Society International proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.

A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK.

Because of the upgraded security and protection it gives, block chain innovation has been embraced in various ventures, including medical care. The ability to give members right and quick admittance to their wellbeing records is perhaps of the main asset in medical care frameworks. Because of block chain innovation's changelessness, decentralization, and security, the medical care industry might encounter change. Versatility, nonetheless, is a significant issue with block bind frameworks because of the record innovation and agreement methods used in those frameworks. Block chain sharding is a critical strategy for tending to the versatility issue in block organizations. Block-chain sharding addresses the versatility issues of block-chains by basically partitioning the organization into discrete gatherings, or shards, that execute exchanges in equal. In this paper, we give a decentralized and more versatile sharding worldview for overseeing medical care information. As per the review, a sharding philosophy based wellbeing application ought to be utilized, with patient solicitations used to make the shards. The suggested strategy can deal with a lot of medical services information while holding more noteworthy exchange speeds and further developed effectiveness, which recommends that it can possibly expand the versatility of blockchain-based wellbeing applications.

The utilization of blockchain technology has seen widespread adoption across various industries, including healthcare. With a growing demand for patient-centric healthcare systems and the imperative to enhance the accuracy of electronic health data, blockchain is poised to significantly impact the healthcare sector. Healthcare providers can leverage blockchain technology to facilitate and monitor peer-to-peer transactions within a decentralized network. Nonetheless, the sharing of electronic health records presents inherent risks, particularly concerning the preservation of patient privacy and ensuring strict access control. Furthermore, the integration of blockchain with the InterPlanetary File System (IPFS) offers potential solutions to limitations associated with traditional cloud storage. While traditional cloud systems may impose centralization dependencies on users, the immutability of blockchain data combined with IPFS provides benefits such as decentralized file storage, cost reduction, and increased transparency. In this paper, we propose a design and security architecture for electronic health records (EHRs) based on blockchain technology. Our approach aims to enable secure and authorized sharing of medical information among healthcare providers, thereby ensuring the integrity and confidentiality of patient health data. Additionally, we conduct a comparative analysis of existing research on the utilization of blockchain technology in secure health data sharing, drawing on various scientific databases for article retrieval and analysis.

Information and Communication Technology (ICT) has become integral to our daily lives. From the way we communicate to how businesses operate. It refers to all communication technologies that enable users to access, retrieve, store, transmit, and manipulate information. It plays a huge role in driving innovation and transforming the society. This research explored the utilization of Information and Communication Technology (ICT) and Learning Management Systems (LMS) in educational sector, providing insight on the prevalent systems, their benefits, challenges, and strategies for effective implementation. Through a descriptive survey design, the research captured insights from both students and teachers, with a sample size of 89 respondents, consisting of 62 students and 27 teachers. Questionnaire was used for data collection. Four research questions guided the study. Mean, standard deviation, frequency, and percentage were used to analyse the research questions. It revealed popular LMS platforms like Canvas, Google Classroom, and Moodle. Additionally, this study sheds light on the transformative impact of ICT and LMS on traditional teaching methods, highlighting their role in facilitating flexible and accessible learning experiences. By identifying barriers and proposing strategies for effective integration, the study offers valuable insights for educators and administrators seeking to harness the full potential of technology in education in order to achieve their goals and objectives and enhance learning outcomes.

This proposition, a novel blockchain framework, integrates neural networks (NN) and federated learning into a Directed Acyclic Graph (DAG) to significantly boost overall performance, robustness, and interoperability. Conventional blockchain systems frequently encounter challenges in these domains, and our methodology is designed to confront these obstacles directly. The framework comprises distinct nodes tasked with transaction management, model aggregation, data synchronization, and governance oversight. These nodes construct local NN models using synchronized historical transaction data, contributing to a global NN model. This architecture guarantees robust and secure transaction processing through NN validation while leveraging the collective wisdom of decentralized model training. Empirical results indicate significant improvements in resilience and scalability compared to conventional blockchain systems, highlighting the transformative potential of this approach in revolutionizing decentralized networks. The decentralized structure of this design promotes increased transparency and trust among all network participants, fostering a more democratic and inclusive environment.

There are several features inherent in blockchain, including decentralized storage, distributed ledger, immutability, security and authentication, and it has shifted away from the hype to be used practically in di erent industries, such as in the healthcare sector. The use of blockchain technology has allowed the provision of improved services to industries. The objective of this paper is to demonstrate how the use of blockchain is influenced by data quality issues in the healthcare industry. The article is structured as a systematic literature review study that uses several articles issued in various databases from onwards. In this review study, articles were chosen and grouped into a single key aspect of the challenge in the healthcare sector. The findings obtained were analyzed based on factors in three domains, classified as issues pertinent to the adoption, operational and technological domains. This review study aims to use the findings to provide support to the practitioners, stakeholders and professionals, whose purpose is to carry out and manage transformation projects pertinent to blockchain in the field of healthcare. In addition, the organizations would be facilitated in their decision-making processes when the potential blockchain users are made to comprehend the implicit factors related to blockchain.

La tecnologia blockchain gracias a sus caracteristicas innatas que son la transparencia, descentralizacion, trazabilidad, entre otras durante los ultimos anos esta siendo ampliamente usada en diversos sectores entre estos el sector agricola. Ademas, en el mundo se pueden encontrar diversas soluciones enfocadas en la educacion, finanzas, transporte y diversas industrias entre estas la industria agricola. Mediante el presente trabajo se busca proponer una aplicacion descentralizada empleando la tecnologia blockchain para lograr la transparencia en el sector agricola, ademas de buscar permitir la gestion descentralizada de la cadena de suministros de forma descentralizada. Asi, en este trabajo se presenta el proceso seguido para el desarrollo de una aplicacion descentralizada para la gestion de la cadena de suministros. Como resultados se presenta la aplicacion descentralizada la cual demuestra que es posible lograr transparentar la cadena de suministros del sector agricola, convirtien...

The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process. The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FL-based electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems. INDEX TERMS Blockchain, federated learning, healthcare informatics, gradient, model aggregation.

The revolutionary advances in machine learning and Artificial Intelligence have enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. Deep learning is the most effective, supervised, time and cost efficient machine learning approach which is becoming popular in building today's applications such as self-driving cars, medical diagnosis systems, automatic speech recognition, machine translation, text-to-speech conversion and many others. On the other hand the success of deep learning among others depends on large volume of data available for training the model. Depending on the domain of application, the data needed for training the model may contain sensitive and private information whose privacy needs to be preserved. One of the challenges that need to be address in deep learning is how to ensure that the privacy of training data is preserved without sacrificing the accuracy of the model. In this work, we propose, design and implement a decentralized deep learning system using peer-to-peer architecture that enables multiple data owners to jointly train deep learning models without disclosing their training data to one another and at the same time benefit from each other's dataset through exchanging model parameters during the training. We implemented our approach using two popular deep learning frameworks namely Keras and TensorFlow. We evaluated our approach on two popular datasets in deep learning community namely MNIST and Fashion-MNIST datasets. Using our approach, we were able to train models whose accuracy is relatively close to models trained under privacy-violating setting, while at the same time preserving the privacy of the training data.

Blockchain is a distributed ledger technology that became popular as the foundational block of the Bitcoin cryptocurrency. Over the past few years it has seen a rapid growth, both in terms of research and commercial usage. Due to its decentralized nature and its inherent use of cryptography, Blockchain provides an elegant solution to the Byzantine Generals Problem and is thus a good candidate for use in areas that require a decentralized consensus among untrusted peers, eliminating the need for a central authority. Internet of Things is a technology paradigm where a multitude of small devices, including sensors, actuators and RFID tags, are interconnected via a common communications medium to enable a whole new range of tasks and applications. However, existing IoT installations are often vulnerable and prone to security and privacy concerns. This paper studies the use of Blockchain to strengthen the security of IoT networks through a resilient, decentralized mechanism for the connected home that enhances the network self-defense by safeguarding critical security-related data. This mechanism is developed as part of the SafeGuarding Home IoT Environments with Personalised Real-time Risk Control (GHOST) project.

Block (N-1) Block (N) Block (N+1) Fig.2-3 Sample blocks in blockchain (source [26]) 2.1.6. Blockchain Types Based on the nature of data accessibility Blockchain can be categorized as Public, Private, Community/Consortium and Hybrid Blockchain. ,