Online social networks (OSN) that gather diverse interests have attracted a vast user base. However, centralized online social networks, which house vast amounts of personal data, are plagued by issues such as user privacy and data breaches, tampering, and single points of failure. The centralization of social networks results in sensitive user information being stored in a single location, making data breaches and leaks capable of simultaneously affecting millions of users who rely on these platforms. Therefore, research into decentralized social networks is crucial. However, blockchain-based social networks present challenges related to resource limitations. This paper proposes a reliable and scalable online social network platform based on blockchain technology. This system ensures the integrity of all content within the social network through the use of blockchain, thereby preventing the risk of breaches and tampering. Through the design of smart contracts and a distributed notification service, it also addresses single points of failure and ensures user privacy by maintaining anonymity. Furthermore, it tackles the scalability concerns associated with blockchain-based systems due to excessive computing resource utilization by improving the off-chain storage structure. By adopting Bloom filters and off-chain storage, it effectively alleviates the burden on on-chain storage. Comparative analysis with related studies demonstrates a minimum of 74% cost savings during post uploads. While the proposed system exhibits slightly slower write performance by 10% compared to existing systems, it showcases 13% faster read performance and achieves an average notification latency of 3 seconds. Thus, this system addresses scalability issues present in blockchain-based systems. It offers a solution that enhances data management not only for online social networks but also for resource-constrained system of blockchain-based IoT environments. By applying this system, data can be managed securely and efficiently.
Muhammad Usman, Muhammad Shahzad Sarfraz, Muhammad Umar Aftab, Usman Habib · 5 authors
Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.
Due to exponential demand in IoT based healthcare, the demand for robust mechanisms to ensure data privacy, security, and scalability with the increasing dependence on cloud-based healthcare systems is immensely felt. Current approaches to dealing with health-care data in cloud settings lack the potency to tackle challenges emanating from the distribution of non-IID data, dynamic access control requirements, and secure cross-chain data analysis. These methods could not provide a holistic solution to adapt with the heterogeneous nature of healthcare data while maintaining advanced privacy and security levels over the distributed networks. In this way, the present work proposes to offer a secure and scalable protocol that is based on the blockchain for healthcare cloud data samples. It integrates the following four new methodologies: Adaptive Federated Learning for Healthcare Data, Secure Homomorphic Blockchain Encryption, Dynamic Attribute-Based Encryption for Healthcare, and Proof of Healthcare Privacy (PoHP) consensus based cross-chain federated Analytics with Zero Knowledge Protocol (ZKP) for healthcare. AFL-HD would work with optimal model training over the distributed healthcare data and thereby handle the challenges that are non-IID in nature, while reducing the communication overhead by 30-40%. SHBE would ensure a 1.5x improvement in encryption and decryption times and also enable secure computations on encrypted data samples. Thus, DABE-HC enables dynamic access control policy management in blockchains, while ensuring access control precision in excess of 99%, with near-instant policy updating. CCFA-HC supports X-blockchain privacy-preserving analytics, thereby reducing the cross-chain communication overhead by 20-30%. In this protocol, therefore, cloud healthcare data management is made more scalable, secure, and private. It allows tackling challenges in the healthcare domain and gives a holistic solution supporting meaningful and secure, efficient, and collaborative healthcare data processing and analytics across distributed environments. The impact of this work is immense in providing a foundation for the next generation of secure healthcare data systems.
The rapid expansion of artificial intelligence (AI) in healthcare has revolutionized diagnostic practices, enabling applications such as tumor detection in medical imaging, genomic analysis, and predictive risk modeling for early disease prevention. Despite these advancements, concerns about the opacity, trustworthiness, and auditability of AI systems remain significant barriers to clinical adoption. Medical practitioners, regulators, and patients increasingly demand systems that not only produce accurate results but also provide verifiable guarantees regarding the integrity and accountability of diagnostic processes. Blockchain technology, with its intrinsic features of decentralization, immutability, and consensus-driven validation, offers a promising solution to these concerns. This manuscript investigates the integration of blockchain-powered verifiable AI models for medical diagnosis. We present a comprehensive framework that leverages federated learning for decentralized training, blockchain for immutable storage and consensus validation, and zero-knowledge proofs for cryptographic verification of model outputs. The proposed system ensures transparent audit trails, enhances data integrity, protects patient privacy, and simplifies compliance with regulatory frameworks such as HIPAA and GDPR. Through simulated case studies in medical imaging and predictive diagnostics, we demonstrate that blockchain integration improves diagnostic verifiability, reduces susceptibility to adversarial manipulation, and fosters patient-centric trust. While slight computational latency is introduced, the trade-off is justified by significantly stronger guarantees of transparency, reproducibility, and ethical accountability. This research underscores the transformative role of blockchain in shaping the future of verifiable AI-driven healthcare, providing pathways toward more reliable, transparent, and equitable medical diagnostic ecosystems.
Jan 1, 2024·Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India
Blockchains like Hyperledger Fabric are comparably faster than public blockchains like Ethereum. These Permissioned Blockchains do not need to consider for in-built security like Bitcoin. The core part of Hyperledger Fabric, the orderer, replicates blocks across the blockchain network. The orderer f
As the Internet and computer technology develop, more gadgets are linked wirelessly, expanding the Internet of Things (IoT). IoT is a huge network of sensors and gateways that links them. IoT devices generate images, music, video, digital signals, and more by interacting with their surroundings. To exchange resources and information, all IoT equipment and apps may connect to the Internet. Everything is connected in our world. Due to the broad deployment and massive size of IoT devices, access control of device resources is problematic. Obtaining IoT device resources unlawfully will have major implications since they include personal and sensitive information. Many systems and situations employ access control technologies to secure resources. Discriminatory, identity-based, and MAC access control schemes are traditional (mandatory access control). However, these centralized methods have single-point failure, scalability issues, poor dependability, and low throughput. IoT devices may belong to several organizations or people, be mobile, and function badly, making centralized access management problematic. Another innovative data management solution is blockchain, which uses distributed storage to stabilize data. A transaction writes the data reading or modification record into a block, and the blocks are connected as a chain using a hash to maintain data integrity. It synchronizes data between nodes via a peer-to-peer network and consensus process, assuring data consistency for blockchain network participants. Zero Trust-Based Blockchain, an open source blockchain development platform, offers more efficient consensus methods, larger throughputs, smart contracts, and support for different organizations and ledgers. Proposed work build the fabric-IoT access control system using Zero Trust-Based Blockchain to apply blockchain technology to IoT access control in this study. Distributed processing and storage for IoT data may solve these critical issues with blockchain. Thus, developing distributed IoT-based e-healthcare services using blockchain technology may have been feasible. FabricIoT can keep records, handle dynamic access control, and solve the IoT access control problem using distributed architecture.
Blockchain technology, with its unique decentralized and tamper-resistant features, is being utilized to address the issue of information silos in traditional electronic healthcare. However, as healthcare data sources become increasingly complex and numerous, the limited scalability and transaction throughput of traditional blockchains result in challenges such as slow processing efficiency and vulnerability to attacks in modern healthcare blockchain systems. To address these issues, we propose a Dynamic Security Resource Configuration scheme based on Medical Consortium Blockchain (MC-DSC). This scheme allows for dynamic blockchain configuration based on the varying urgency levels of data, enhancing data processing efficiency. It ensures the security of the data processing process through identity control and data encryption methods. Experimental results demonstrate that, compared to existing blockchain configuration algorithms (SsHealth and Medge-Chain), the proposed scheme achieves approximately a 15% performance improvement by dynamically configuring the blockchain for three data types (secure, urgent, and normal). Additionally, the security module accounts for only 7% of the total time overhead, efficiently safeguarding the security of healthcare data while effectively handling data with different urgency levels.
Brijesh Kumar Chaurasia, Vinay Rishiwal, Mano Yadav, Man Mohan Shukla · 5 authors
In the internet of vehicles networks (IoV-N), vehicles exchange messages to improve traffic efficiency using fog and edge computing. Vehicles may make decisions based on exchanged messages in IoV-N. Therefore, security and key management in the IoV-N are the primary concerns. In this paper, key management for social IoV-N using blockchain is presented. This work also utilises non-fungible tokens (NFTs) and interplanetary file systems (IPFS) to store and secure records such as keys, issuer identities, etc. on behalf of trusted static authorities such as the city-level transport authority (CTA). The paper also introduced bilinear pairing for key generation and secure exchange in the IoV-N. CTA makes up the key generation process at the fog layer to reduce latency, and the blockchain is stored in a data server at the cloud layer. Extensive simulation and results show that key management for social IoV-N is a secure, fast, and viable solution.
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.
The exponential growth of heterogeneous Internet of Things (IoT) networks has amplified demands for secure, scalable, and sustainable transaction verification mechanisms. Traditional blockchain consensus protocols, such as Proof-of-Work (PoW), offer robust security but impose prohibitive energy costs, limiting their viability for resource-constrained IoT environments. Proof-of-Stake (Po’s) and lightweight consensus schemes improve efficiency but often compromise scalability or fairness. To address this trade-off, this study introduces an energy-aware blockchain consensus framework enhanced by graph neural networks (GNNs) for sustainable, scalable verification across heterogeneous IoT ecosystems. In this approach, GNNs are applied to dynamically model IoT device interconnections, enabling efficient clustering, adaptive leader election, and optimized consensus pathways. By learning the structural and temporal patterns of IoT networks, GNNs reduce redundant computations and allocate verification tasks intelligently, minimizing energy consumption while maintaining security. The consensus framework integrates energy profiling of devices with predictive workload balancing, ensuring equitable participation across diverse hardware capacities. Blockchain provides the foundation for immutable, decentralized trust, while the GNN-enhanced consensus mechanism improves throughput, latency, and energy efficiency in large-scale deployments. Simulation studies of smart grids, industrial IoT, and urban sensor networks demonstrate measurable improvements in energy savings, scalability, and fault tolerance. The proposed architecture contributes to the vision of sustainable blockchain systems that can operate effectively in energy-sensitive, heterogeneous IoT contexts. By fusing blockchain’s decentralized trust with GNN-based intelligence, the framework offers a pathway toward greener, more scalable transaction verification tailored for next-generation IoT infrastructures.
The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAW_MOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency.
Enhanced by blockchain and cloud-edge-end cooperation, an edge computing network is capable to provide IoT (Internet of Things) devices higher task processing performance and better security and privacy guarantee. However, the joint resource management for both the task offloading and the blockchain services was less fully studied by existing works. To this end, in this paper, we focus on the task processing delay and energy consumption optimization problem in a multi-device and multi-base-station cloud-edge-end cooperative network. The task offloading, transmit power allocation, transmission rate allocation, and computing resource allocation are jointly optimized to minimize the long-term average total task processing delay of the tasks of all the devices while keeping the stability of the energy consumption of the devices and guaranteeing that the block mining speed matches the task offloading processes. We transform the optimization problem based on the Lyapunov optimization theory, and then design a hybrid deep reinforcement learning (DRL)-based algorithm. We decompose the problem into multiple sub-problems, and then embed multiple fast numerical methods into the twin delayed deep deterministic policy gradient (TD3) architecture as optimization subroutines to improve the learning performance of the DRL model. We also design a distributed deployment scheme for the algorithm and analyze the algorithm complexity. We demonstrate the superior performance of our algorithm in comparison with 5 reference schemes via extensive experiments in 7 scenarios.
Blockchain is known for its potential to create high transparency, decentralization, and unprecedented security. However, it is still confronted with scalability problems which pose a significant barrier to its wide adoption. Scalability constraints result in high transaction fees, high latency, and low throughput in many blockchain networks. To handle this bottleneck, several scaling solutions have been developed. Recent research has explored hybridization of scaling solutions, paving the way for major scaling innovations. This article analyzes how parallel and aggregated scaling methods can be integrated to achieve exponential scalability in blockchain networks. We examine fundamental concepts, techniques, implementation strategies, and then propose ZKShard, a paradigm based on zero-knowledge proofs and sharding. In the proposed system, key components such as parallelization, communication, aggregation, user experience and interaction were examined. We also explored ZKShard applications in domains such as Decentralized Finance (DeFi), New Financial Technologies (NFTs), and Supply Chain Management. Finally, we discussed research challenges and future directions based on security, cross-shard communication, data availability, smart contracts, and consensus mechanism.
Since the smart contracts are being introduced, they are being subjected to various vulnerabilities. These vulnerabilities pose a major threat to participant's money and cause conflicts. Smart contracts are self executing contracts that use blockchain technology to transfer values among peers in which there is no control of a central authority to run and execute transactions. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. As technology advances, many reentrancy solutions have been implemented and various detection approaches have been proposed. In this paper, it proposed a “Smart MutexLock” mechanism to prevent Ethereum reentrancy. Since the smart contracts on blockchain platforms like Ethereum do not directly support mutex locks due to their deterministic and asynchronous nature, it have used mutex locks in an effective and secure manner. The mutex mapping is used to keep track of whether a particular address is currently in a transaction to prevent reentrancy. The mutex variable is set to true before any external call, preventing further calls until the transaction is completed. Once the transaction is complete, the mutex variable is set back to false and user is notified to ensure a safe transaction
Blockchain, a decentralized and immutable distributed ledger technology (DLT), encompasses a sequential arrangement of records accompanied by chronological timestamps. Utilizing decen- tralization technology has emerged as a potent paradigm for fostering trust within entities lacking inherent trustworthiness, thereby reliably enabling verification. Driven by the recent progress in multi-access edge computing (MEC) and artificial intelligence (AI), the integration of blockchain with edge intelligence has emerged as a developing technological paradigm within the realm of the Internet of Things (IoT). In this scholarly discourse, we shall comprehensively examine the func- tioning of blockchain-enabled edge intelligence within the Internet of Things (IoT) domain. Our primary objective is to elucidate the intricacies of this technological framework while concurrently discerning the current trends. Furthermore, we shall endeavor to proffer unresolved matters that warrant further scholarly investigation. To provide a more precise elucidation, we shall give the reader a foundational understanding of Distributed Ledger Technology (DLT), Multi-access Edge Computing (MEC), and Artificial Intelligence (AI). Secondly, we shall thoroughly examine extant scholarly literature to discern nascent patterns and developments within this particular domain of inquiry. Lastly, we shall engage in a discourse about unresolved quandaries and cavities in the existing body of research, thereby delineating potential avenues for future investigations. Integrating blockchain technology with edge intelligence is anticipated to become a pivotal facilitator for the Internet of Things (IoT) in the foreseeable future. This amalgamation holds the potential to instill trust and intelligence, thereby effectively catering to the intricate requirements of various industries and society at large.
Vertical Federated Learning (VFL) effectively addresses the issue of data isolation, which makes data mining secure. Most VFL implementations rely on a single server or third party for training, which will be terminated if the server or third party fails. In addition, the model accuracy trained by VFL depends on the quality of the client’s local features; nevertheless, the client’s local feature quality is difficult to verify. There exists a chance that the features owned by the client are irrelevant to the model or the intermediate results submitted by the client are inaccurate, such that the model’s accuracy will be seriously affected. In order to solve the single point failure and model accuracy issues in VFL, this paper first proposes a Blockchain – based Decentralized VFL (BDVFL) training model. With the integration of blockchain and the VFL training process, the nodes within the blockchain are categorized into non-training and training nodes. Our method focuses on the scenario in which all training nodes possess labeled data and actively engage in the training procedure of VFL. To be specific, first, each client utilizes local features and initial models to carry out forward activation and generate intermediate results. Second, we randomly choose a training node and combine it with the intermediate results from all clients to formulate the loss function. Finally, each client updates the local model by using the gradient. To protect the raw features, a blinding factor is utilized for safeguarding the intermediate results submitted by the client, such that the training nodes cannot infer the local features from intermediate results. To mitigate the interference of irrelevant training outcomes from clients on the model’s accuracy, we propose a verifiable aggregation method to assess the validity of the intermediate results submitted by the clients. We have conducted both theoretical and experimental analysis, and the results demonstrate the effectiveness of the proposed method.
D. Geethanjali, N. Umasankari, P. Umamaheswari, Siddikeshwer Reddy
The distributed consensus algorithm which is a collection of guidelines or protocols known as consensus algorithms enable dispersed nodes to decide on a value or course of action based on messages from other nodes as well as their own local data. The following characteristics are the goals of consensus algorithms: fault-tolerance, validity, agreement, and termination. This paper provides the outline and study about the different consensus algorithms in blockchain. The proposed paper describes about the working mechanisms of different consensus algorithms such as proof of work, proof of stake and etc., Also explains the requirement of consensus algorithms, advantages and disadvantages of each consensus algorithm.
In today’s interconnected digital era, email stands as a primary platform for communication across various fields, such as business, academia, and personal interactions. Since it operates on shared mediums, email is inherently susceptible to interception and misuse of sensitive information. To cover this issue, Pretty Good Privacy (PGP) ensures the confidentiality of email contents. However, while PGP effectively encrypts the body of messages, a significant vulnerability emerges during key sharing. Blockchain technology comes with its immutability aspect. Once data is embedded into the blockchain, modifying it becomes exceedingly challenging. This makes it a potential safeguard against the vulnerabilities in PGP’s key sharing mechanism. Additionally, smart contracts remove the requirement of a Man-in-the-Middle for sharing key, which enhances the sharing key’s security and builds trust among users. In this paper, we integrate the strengths of blockchain into the PGP encryption process, specifically targeting the key sharing vulnerability. Also, we adopt Elliptic Curve Cryptography (ECC) to ensure the confidentiality of the PGP’s key. First, generate the PGP’s key. Then, encrypt it using ECC. After that, deploy a smart contract and continue with the encrypted PGP’s key transactions in blockchain networks. Through this integration, we aim to enhance the security, ensuring both confidentiality and integrity of the PGP’s key. We demonstrated that the proposed system is workable by experimental observation.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Salna Joy, Baby Chithra R, P S Neethu, Anju M. I · 7 authors
The healthcare sector deals with extremely sensitive information that must be administered in a safe and confidential way. The objective of the proposed framework is to utilize Blockchain Technology (BT) for tracking medical prescriptions and the implementation is carried out using the Hyperledger Fabric platform, an enterprise-grade open-source distributed ledger technology platform designed for Bigdata applications. Multiple entities, including patients, e-pharmacies, pharmacies, doctors and hospitals can establish connections by introducing several nodes in the Fabric chain. A web-centered application is provided for doctors, connecting them with participating pharmacies, hospitals and e-pharmacies through which, they can share patient prescription. Pharmacies and e-pharmacies have access to this data and can notify patients about the availability of prescribed medicines. Additionally, reminders for refills, such as heart medication, can be sent for patients requiring long-term medication. Patients can also try with nearby pharmacies and the availability of their prescribed medicines. The inclusion of a wallet feature in the application enables patients to use mobile tokens for making purchases. Patient data is treated with the utmost confidentiality, kept private, and accessed only upon request and with the consent of the concerned parties. This privacy is ensured through the use of zero-knowledge proof. Patients retain access to their complete medical history, facilitating interactions with doctors without the need for repetitive information sharing.
Blockchain based federated learning is a distributed learning scheme that allows model training without participants sharing their local data sets, where the blockchain components eliminate the need for a trusted central server compared to traditional Federated Learning algorithms. In this paper we propose a softmax aggregation blockchain based federated learning framework. First, we propose a new blockchain based federated learning architecture that utilizes the well-tested proof-of-stake consensus mechanism on an existing blockchain network to select validators and miners to aggregate the participants' updates and compute the blocks. Second, to ensure the robustness of the aggregation process, we design a novel softmax aggregation method based on approximated population loss values that relies on our specific blockchain architecture. Additionally, we show our softmax aggregation technique converges to the global minimum in the convex setting with non-restricting assumptions. Our comprehensive experiments show that our framework outperforms existing robust aggregation algorithms in various settings by large margins.
Existing zero-watermarking algorithms for remote sensing images heavily rely on traditional feature extraction techniques, which are vulnerable to targeted attacks and lack discriminability for images captured by different sensors or at different time periods in the same geographical area. To address these limitations, this paper proposes a novel watermarking algorithm based on blockchain and Stacked Denoising Autoencoder (SDAE) to achieve lossless copyright protection for high-resolution remote sensing images (HRRS). The algorithm utilizes SDAE to extract deep and robust features from local square feature regions (LSFR) for watermark construction. Moreover, the algorithm incorporates a watermark registration scheme designed with Hyperledger Fabric and IPFS to ensure secure and trustworthy registration of watermarks and associated parameter information, enhancing the algorithm's uniqueness. Experimental results demonstrate the effectiveness of the proposed algorithm against various watermark attacks and its high discriminability for similar images. This algorithm holds significant potential for wide-ranging applications in the field of lossless copyright protection for HRRS, effectively safeguarding the commercial interests of data providers.
Open access
Advanced Steganography and Watermarking Techniques