Abstract Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.
Eranga Bandara, Peter Foytik, Sachin Shetty, Ravi Mukkamala · 8 authors
The main challenges in the 5G/6G network slicing are resource sharing, network slice orchestration, and network optimization in the 5G ecosystem. This paper proposes a novel architecture for a dynamic network slice broker and marketplace named “SliceGPT” that leverages Custom-Trained OpenAI GPT-3.5 LLM, blockchain and NFTs to enable collaboration between different stakeholders in the 5G ecosystem to address these challenges. The platform enables different stakeholders in 5G network slicing (e.g., cloud providers, network operators, RAN providers, and transport network providers) to share and rent their resources to create customized network slices that meet the specific requirements of 5G applications. The orchestration of network slices is managed through blockchain smart contracts, and the resulting network slices are encoded as NFT tokens and made available for purchase in a decentralized NFT marketplace. Customers can select and purchase the network slices that best meet their needs by paying either crypto or flat currency. Revenue generated through the sale of network slices is distributed among different providers, facilitating a fair and efficient marketplace. Intelligent network slice optimization is accomplished through the utilization of a custom-trained GPT-3.5 LLM(which powers the ChatGPT). This LLM can generate valuable insights and recommendations from extensive network datasets, contributing to the optimization of network slices for enhanced performance and efficiency. A prototype of SliceGPT has been implemented with FreedomFi 5G gateway, OpenAirInterface 5G core, OpenAI GPT-3.5-turbo model, LlamaIndex and Langchain. To the best of our knowledge, this is the very first research endeavor to incorporate the GPT LLMs for optimizing 5G/6G network slicing,
Abdul Muqsit Haji Jafari, Ravi Kumar Patchmuthu, Sharul Tajuddin
The COVID-19 pandemic has presented numerous challenges around the world, including lockdowns, remote working and studying, and travel restrictions imposed by governments to curb the spread of the virus. As vaccines have become more widely available, restrictions have begun to ease for those vaccinated. Many countries use paper-based COVID-19 vaccination certificates to prove vaccination status. However, traditional certificates are vulnerable to forgery and counterfeiting. We developed a blockchain-based system where vaccination certificates are stored and accessed via smart contracts on an Ethereum blockchain. The certificates are stored in a tamper-proof, decentralized manner, ensuring secure verification of vaccination status. This research project successfully designed and implemented a system that securely stores and verifies vaccination certificates using blockchain, demonstrating the benefits of this approach. According to our knowledge, this is the first kind of research initiative in Brunei to develop a blockchain-based immutable COVID-19 Vaccination certificate. Despite its advantages, blockchain still has its flaws, particularly in scalability and adoption, which should be considered for further optimization of the proposed system.
Ken Naganuma, Masayuki Yoshino, Noboru Kunihiro, A. Inoue · 6 authors
In recent years, the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) have drawn significant attention as privacy-enhancing technologies in various domains. A post-quantum designated verifier type zk-SNARK for Boolean circuits was proposed by Gennaro et al. in ACM CCS '18. However, this scheme does not include arithmetic circuits and they claim that a construction of post zk-SNARKs for arithmetic circuits as open problem. In this paper we give answers to this problem. Our first proposal is based on the data structure used in Pinocchio, a previous study, and can be easily implemented using the existing Pinocchio-based systems. In our second proposal, which also employs QAPs, the zero-knowledge proof comprises three learning with errors (LWE) ciphertexts, and the size of the proof is smaller compared with that of the first proposal. Our second proposal is also more efficient than the first one or all other known post-quantum zk-SNARKs.
Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction, and realizes privacy protection. At the same time, the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price. The smart contract is used to complete the verification and game bargaining process, and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial. Finally, by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations, it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment. The experimental results show that the model improves the credibility and fairness of medical data transactions, maximizes social benefits, encourages more patients and medical institutions to participate in the circulation of medical data, and more fully taps the potential value of medical data.
The IOTA Tangle cryptocurrency represents the next evolution in distributed ledger technology, designed to facilitate transactions in the Internet of Things applications. Compared to traditional blockchain architectures, the IOTA presents attractive characteristics, including high scalability, zero-fee transactions, higher achievable transactions per second, and lower energy consumption. The consensus mechanism in IOTA 1.0 and 1.5 relied on a centralized coordinator solution for transaction validation. IOTA 2.0, designed in 2020, is the first fully decentralized network that supports smart contracts. This paper provides a comprehensive overview of the IOTA Tangle 2.0.
Artificial Intelligence has largely occupied various sectors in the world. A huge number of business companies have incorporated several machine learning algorithms for day-to-day decision making. With increasing applications of AI algorithms, the concerns regarding its outcomes have also increased due to bias. In AI algorithms, bias occurs due to multiple reasons including incomplete data, skewed data, human error and so on. These algorithms have the tendency to amplify partially and discrimination in the results instead of benefiting them. This makes it compulsory for the algorithms to be audited. Currently, AI algorithm auditing processes have several challenges including tendency of biases to be deeply ingrained into the system, making these difficult to mitigate; lack of transparency in decision making and many more. This study presents the emerging technology of blockchains to be a viable solution to the existing problem. It comprehensively discusses the suitability of blockchains for transparency in the process of algorithm auditing which is bound to easily capture the issue and the layer consisting it. Consequently, the process of algorithm auditing will be more convenient and more productive. Moreover, this review also discusses some potential challenges that need to be addressed and some future recommendations for this integration.
Ahmed Alhusayni, Vijey Thayananthan, Aiiad Albeshri, Saleh Alghamdi
Smart devices are connected to IoT networks and the security risks are substantial. Using blockchain technology, which is decentralized and distributed, 5G-enabled IoT networks might be able to tackle security issues. In order to simplify the implementation and security of IoT networks, we propose a multi-level blockchain security model. As part of the multi-level architecture, the communication between levels is facilitated by clustering. IoT networks define unknown clusters with applications that utilize the evolutionary computation method coupled with anatomy simulation and genetic methodologies. Authentication and authorization are performed locally by the super node. The super node and relevant base stations can communicate using local private blockchain implementations. A blockchain improves security and enhances trustworthiness by providing network authentication and credibility assurance. The proposed model is developed using the open-source Hyperledger Fabric blockchain platform. Stations communicate securely using a global blockchain. Compared to the earlier reported clustering algorithms, simulations demonstrate the efficacy of the proposed algorithm. In comparison with the global blockchain, the lightweight blockchain is more suitable for balancing network throughput and latency.
The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.
The Blockchain-based Electronic Health Record (B-EHR) system represents a significant advancement in healthcare data management. Concerns over data confidentiality and security have become increasingly critical in the healthcare sector, given the need for immediate data accessibility. Traditional centralized systems face accessibility issues, necessitating a transformative solution, and blockchain technology emerges as a promising candidate. This research introduces a patient-controlled, blockchain-based system that efficiently manages and safeguards individuals' health-related data. By harnessing the Ethereum network and utilizing tools such as Ganache, Solidity, and web3.js, this system takes a systematic approach to overcome the limitations of centralized systems. Smart contracts, the basis of blockchain technology, serve as the backbone for storing and processing patients' data in a decentralized manner. Transactions are conducted securely through these smart contracts, ensuring patient privacy and data security. Notably, any modifications to transactions can be verified and propagated across the entire distributed network, enhancing data integrity. Complementing this system is a cryptocurrency wallet like MetaMask, providing a centrally controlled repository where records can be swiftly accessed and secured by authorized individuals, including doctors and patients. This integration significantly improves data accessibility and security within the healthcare domain. Ultimately, this research aims to leverage blockchain technology for simultaneous data retrieval, enhancing efficiency, credibility, and accessibility. It offers a robust framework for securely storing data with tailored access permissions and facilitates the safe transfer of patient medical records. In essence, it introduces a swift and secure health record system and an innovative protocol, promoting greater transparency and ownership of sensitive data in the healthcare sector through blockchain integration.
The rapid growth of the Internet of Things (IoT) has raised security concerns, including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security (TLS) protocol. This paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining efficiency. This approach involves using blockchain sharding, which enables higher scalability, improved performance, and reduced computational overhead compared to traditional blockchain approaches, making it well-suited for resource-constrained IoT environments. This approach leverages Ethereum blockchain's smart contract mechanism to ensure trust, accountability, and user privacy. Specifically, we introduce a shard-based consensus mechanism that enables improved security while minimizing computational overhead. We also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain network. The proposed approach is analyzed for usability, including metrics such as bandwidth consumption, CPU usage, memory usage, delay, access time, storage time, and jitter, which are essential for IoT application requirements. The analysis demonstrated that the approach reduces resource consumption, and the proposed system outperforms TLS and existing blockchain approaches in these metrics, regardless of the choice of the MQTT broker. Additionally, thoroughly addressing future research directions, including issues and challenges, ensures careful consideration of potential advancements in this domain.
More and more IoT use cases require trustworthy computing from cloud/back-end services, which cannot necessarily provide a fully trusted execution environment, data immutability, and traceability. The integration of IoT with the blockchain technology is one of the most promising solutions to achieve the previously mentioned features in the IoT networks. Researchers are also interested in integration solutions, and several solutions are already present in the scientific literature. However, there are still some uncertainties in establishing a direct and effective interaction between an IoT device and the given blockchain. In this work, we propose the first IoT hardware architecture model designed to accelerate time-consuming operations of IoT-Blockchain. The proposed IoT hardware architecture model is programmed in SystemC-TLM and can provide a significant reduction in execution time, 53% and 18% when running Hyperledger Sawtooth and Ethereum applications, respectively.
Proof of stake is one of the consensus protocols in Blockchain technology. Within PoS, there are two common strategies employed for validator selection: Coin-age selection and Randomized block selection. However, both of these strategies have drawbacks when it comes to ensuring a fair selection of validators, leading to a phenomenon known as the "rich getting richer" syndrome. The paper proposes a new consensus algorithm that introduces a novel parameter: The time stamp of staking tokens. Staking tokens are those minted by validators to participate in the staking process. By considering the timestamp of staking tokens minted before participating in the staking procedure, the proposed algorithm aims to address the shortcomings of the existing strategies. The algorithm seeks to eliminate the loopholes in the coin-age selection method and enhances it in terms of precision. Additionally, it aims to increase the randomness in choosing a validator, thereby mitigating the rich-getting-richer syndrome.
Smart health care will be a major application in future smart cities. Timely and precise delivery of patients’ data to their medical consultant, to allow the necessary actions, is one of the basic needs in health care systems. Blockchain technology, with the provisioning of recording and tracking of data blocks, guarantees secure and error-free data delivery. The vital sign data from patients’ sensors are placed in different data blocks. To become a part of the blockchain, the block must contain a valid key, based on a hash function. Mining nodes with high processing capabilities generate the required key using a 32-bit number, known as a nonce, which is changed for every new block. Finding a nonce that meets the hash function requirements is a time-intensive process in blockchain technology and is performed by several fog mining nodes. However, an efficient resource allocation that results in the fair placement of data in these fog mining nodes, while maintaining the priority and sensitivity of patients’ data, is a challenge. This work proposes two algorithms for the resource allocation of mining nodes. The first algorithm uses a load balancing technique to distribute the load of nonce computing tasks. The second algorithm utilizes the knapsack algorithm to allocate the caching space of the mining nodes. The simulation results highlighted that the proposed resource allocation techniques outperformed the existing techniques, in terms of quick mining of the most sensitive patient data blocks.
People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.
Fangyu Gai, Jianyu Niu, Mohammad M. Jalalzai, Seyed Ali Tabatabaee · 5 authors
Sidechains allow transaction dissemination and execution outside the blockchain main network (i.e., the mainchain), enabling a scalable, efficient, and secure financial infrastructure for the Internet of Things (IoT) without trusting any central authority. Existing sidechains either have online requirements or rely on intensive computation on a central operator, which does not meet the needs of IoT for dynamic changes and high performance. This article proposes an alternative sidechain construction, called Cumulus, which meets the needs of IoT by leveraging the classic Byzantine fault-tolerant (BFT) consensus protocols, such as PBFT, that have commonly been applied in permissioned blockchains. Cumulus builds BFT-based sidechains atop public blockchains (e.g., Ethereum) using smart contracts and ensures the bidirectional safety of users’ assets. Cumulus sidechains periodically interact with the mainchain and submit checkpoints through representatives selected in an efficient and decentralized manner. The experiments show that Cumulus sidechains outperform rollup-based sidechains, and state-of-the-art sidechain constructions, achieving two and three orders of magnitude improvement in throughput and latency while retaining comparable operational cost.
Smart contracts refer to small programs that run in a decentralized blockchain infrastructure. The blockchain system is trustless, and the determination of common variables is done by consensus between peers. Developing applications that require generating random variables becomes significantly challenging—for instance, lotteries, games, and random assignments. Many random number generators (RNGs) for smart contracts have been developed for the decentralized environment. The methods can be classified into three categories: on-chain RNG, Verifiable Random Function (VRF), and the Commit–reveal scheme. Although the existing methods offer different strengths and weaknesses, none achieves the three important requirements for an ideal RNG solution: security, applicability, and cost efficiency. This paper proposes a novel RNG approach called Native VRF, which offers application development simplicity and cost efficiency while maintaining strong RNG security properties. Experimental results show that Native VRF has the same security properties as the widely used RNG methods, i.e., Randao and Chainlink VRF. On top of that, our work offers a much simpler setup process and lower hardware resources and developer expertise requirements. Most importantly, the proposed Native VRF is compatible with all Ethereum virtual machine (EVM) blockchains, contributing to the overall growth of the blockchain ecosystem.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques