Private Blockchain Research Articles

Blockchain Privacy and Self-Regulatory Compliance: Methods and Applications

Blockchain Privacy and Self-Regulatory Compliance: Methods and Applications

Multi-task learning for PBFT optimisation in permissioned blockchains

Finance, supply chains, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT), which tolerates up to one-third of Byzantine nodes, performs within partially synchronous systems, and has superior throughput compared to other protocols. It has, however, an important bandwidth consumption: 2N(N−1) messages are exchanged in a system composed of N nodes to validate only one block.It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security, rapidity, and availability. In this paper, we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus. It reflects their level of security, rapidity, and availability throughout the consensus. We first investigate different Single-Task Learning (STL) techniques to classify the nodes within our dataset. Then, using Multi-Task Learning (MTL) techniques, the results are much more interesting, with classification accuracies over 98%. Integrating node classification as a preliminary step to the PBFT protocol optimizes the consensus. In the best cases, it is able to reduce the latency by up to 94% and the communication traffic by up to 99%.

Public and Private Blockchain Infusion: A Novel Approach to Federated Learning

Public and Private Blockchain Infusion: A Novel Approach to Federated Learning

Blockchain Interoperability in Data Exchange Logistics Integration

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange.

Enhancing Global Blockchain Privacy via a Digital Mutual Trust Mechanism

Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods.

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

A Decentralized Authenticated Key Agreement Scheme Based on Smart Contract for Securing Vehicular Ad-Hoc Networks

Since the communication channels in vehicular ad-hoc networks (VANETs) are wireless and open, malicious adversaries can monitor or fabricate messages transmitted across them. To secure vehicular communications, an authenticated key agreement (AKA) scheme needs to be designed for VNAETs. Traditional VANETs AKA schemes require the trusted authority (TA) to authenticate the legality of message and corresponding sender. However, the TA in these schemes is vulnerable to suffer from single-point-of-failure issues. Some blockchain-based VANETs AKA schemes have been proposed recently to address the deficiency. However, these schemes rely on the consortium or private blockchain in which TAs are still required for key generation, resulting that the practicality is limited. To solve the issue, we design a smart contract-based VANETs AKA scheme, where the AKA algorithm of our proposed scheme is implemented on smart contract deployed on a public blockchain system and the TA that is responsible for key generation will not be required. The security proof and analysis show that our proposed scheme satisfies the session-key semantic security and essential security and privacy requirements, respectively. The performance analysis demonstrates that our proposed scheme outperforms existing blockchain-based VANETs AKA schemes.

FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning

Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage attacks while ensuring the accuracy of aggregation results, its widespread adoption in blockchain privacy preservation is hindered by the reliance on a trusted key generation center and secure transfer channels. Conversely, coverless steganography schemes effectively ensure the covert transmission of sensitive information across insecure channels. However, their incompatibility with HE‐PPFL arises from the lossy extraction process. To address these challenges, we present a decentralized federated learning privacy‐preserving framework based on the Lifted ElGamal threshold decryption cryptosystem. We introduce a reversible steganography method tailored to safeguard gradient privacy. Furthermore, we introduce a lightweight, secure blind aggregation algorithm founded on the Raft protocol, which serves to protect gradient privacy while substantially mitigating computational overhead. Finally, we provide rigorous theoretical proof of the security and correctness of our proposed scheme. Experimental results from four public data sets demonstrate that our proposed scheme achieves a 100% extraction accuracy without the need for lossless methods, while simultaneously reducing the computational cost of ciphertext gradient aggregation by at least three orders of magnitude. The FedSteg framework is publicly accessible at https://github.com/Xumeili/FedSteg. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Artificial intelligence driven cognitive optimization and predictive analysis using blockchain privacy-based machine learning model

According to the cognitive radio paradigm, spectrum sensing, decision-making, sharing, and mobility phases can be integrated to enable both authorised and unauthorised users to coexist in the radio-electric spectrum to the fullest extent possible. Using a blockchain privacy model and ML approaches in a B5G network, this study suggests a new approach to cognitive network optimisation and predictive analysis which stands for blockchain privacy-based transfer encoder neural networks. One further step is to optimise the network using binary swarm optimisation (BSMO). Bandwidth efficiency, throughput, forecast accuracy, and quality of service are the experimentally measured variables for a given number of channels and users. An innovative QoS-based optimisation phase and two separate decision-making procedures are proposed for usage in a proactive approach. One use of artificial neural networks (ANNs) is the prediction of future traffic loads for different radio access technologies (RATs), which employ different bands of the electromagnetic spectrum.

Organic production competitiveness: A bi-level model integrating government policy, sustainability objectives, and blockchain transparency

The government subsidizes organic production to reduce the cost of production and encourage consumers to buy it. However, some organic producers do not adhere to organic production standards, which erodes customer confidence in these products. Companies use blockchain technology to display the entire production process to customers and increase transparency. This paper presents a bi-level model designed to investigate the competition between conventional and organic producers. The government wields more power than the producers and makes decisions at the upper level of the model. The demand function for each conventional or organic product is contingent upon the decisions made by its producer, as well as those made by competing producers, leading to competition at the lower level of the model. The model examines the effects of the government's role, the growth of export sales abroad, the type of information technology used in decision-making, and sustainable goals. The public and private versions of blockchain technology for organic production are compared in terms of transparency, costs, and environmental concerns. The proposed model is converted into a single-level model using the Karush-Kuhn-Tucker (KKT) approach. The results show that a combination of economic, social, and environmental goals can achieve a sustainable equilibrium. The comparison of blockchain technology scenarios shows that the utilization of private blockchains enhances producer profitability and reduces GHG emissions, albeit accompanied by an increase in additive usage. The sensitivity analysis of the public blockchain indicates that while consumers find an increased cost of use acceptable, it diminishes the economic appeal relative to private blockchain.

An autonomous blockchain‐based computational broker for e‐science

AbstractBlockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peer‐to‐peer payment systems) and smart contracts. They can have a wide range of application areas in e‐Science due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchain‐based middleware system for volunteer computing and providing data resources for e‐Science. The eBlocBroker infrastructure connects requesters who need to combine applications (jobs) with datasets and run them via an Ethereum‐based private blockchain network (Bloxberg) on providers that utilize computational and data resources on clouds or home servers. It uses cloud storage, such as B2DROP, IPFS, or Google Drive, to store and transfer data between requesters and providers. Each provider utilizes the Slurm workload manager to execute jobs submitted through eBlocBroker. In this paper, we demonstrate how an autonomous organization programmed as a smart contract can be used to deploy a marketplace that supports data and computation‐intensive research projects. We propose a cost model implemented as a function in the smart contract which calculates and records computation and dataset usage costs. We develop a Python‐based system to communicate with eBlocBroker and orchestrate jobs' execution on the provider's end. We present eBlocBroker's features, infrastructure, implementation, algorithms and experimental results.

Delving into and Implementing Blockchain-related Technologies

Blockchain, birthed with the advent of Bitcoin, stands as a revolutionary decentralized digital ledger technology, casting light on the pathways of transparency, security, and efficiency across diverse sectors. Rooted in a blend of sophisticated technologies – from P2P network technology and encryption to timestamping – blockchain ensures secure, immutable transactions spanning its distributed nodes. Beyond its rudimentary role in Bitcoin, the technology has matured, fueling other digital currencies and leading to the emergence of private blockchains tailored for specific organizational processes. In its architectural framework, blockchain is layered with components like data, network, and consensus. Moreover, its versatile application layer introduces functionalities such as digital currencies and the dynamism of smart contracts. Yet, with all its promise, the foray of blockchain into domains such as commercial banking is still in its infancy, grappling with hurdles related to regulatory acceptance and broad market recognition. At the heart of blockchain lie its blocks: repositories of transactional data linked in a sequential chain. This design ensures paramount data integrity, upheld by stringent cryptographic principles. As industries gradually awaken to its potential, blockchain's evolutionary journey is bound to unfold further nuances, driving transformation in ways yet to be fully realized.

Adoption of Blockchain Technology Framework for Addressing Counterfeit Drugs Circulation

Counterfeit drugs are a problem both in developed and developing countries. Nigeria is a developing country experiencing the problem of counterfeit drugs and as such is the use case for this research work. The negative effects counterfeit drugs have on human health and economy of the country calls for urgent attention. One amongst many reasons why counterfeit drugs are still in circulation in Nigeria is the unmonitored drug distribution system. Drugs throughout their shelf life moves from one owner to another, from the importer/manufacturer to Distributor, wholesaler to government/private healthcare sectors and then to the patients. The current drug distribution system in Nigeria is a manually, unmonitored system, where the regulatory agency body National Agency for Food and Drug Administration control (NAFDAC) does not have detailed record on the movement of drugs from either the importer or manufacturer to the patient in the system. In this study blockchain technology framework that can handle the drug distribution chain was discussed, since blockchain technology offers immutability, transparency and tamper proof, it functionalities are what intrigued the study as it can help monitor the movement of drug in the distribution chain. The proposed system is a prototype demonstrating the hashing mechanism with a deployed smart contract and how it can help in showing the authenticity of a drug. The blockchain framework is a private blockchain, where participants on the network are permitted to have access to the network by the regulatory agency. The system was evaluated based on it’s effectiveness from users reports using the security, user friendly, response time, flexibility, data integrity checks, robust, hashing mechanism functional as the benchmark. The actual test result equates the expected test result, showing that the system is efficient.

An Online Multi-Item Auction With Differential Privacy in Edge-Assisted Blockchains

An Online Multi-Item Auction With Differential Privacy in Edge-Assisted Blockchains

Document Storage and Verification System Using Blockchain Technology

A banking or identity provider can set up a customer identification data verification process between reliant parties with the use of the electronic know your customer (e-KYC) system. Due to its high degree of accessibility and availability and its efficient resource usage, the majority of banks choose to implement their e-KYC system on the cloud. All of the KYC procedures used by banks rely on encryption, which is a cumbersome process that may cause consumer data to be disclosed to unaffiliated financial institutions. Blockchain technology can be used to increase the efficiency of this system because it can automate a lot of human labor and is impervious to attacks of all kinds. The distributed ledger and immutable blockchain block make the perfect addition to the KYC process. The use of smart contacts can automate the identification of fraud. Any kind of KYC can be used to store information related to KYC identity. Consequently, financial institutions can establish a shared private blockchain on their premises for the purpose of document validation. This allows the user to maintain control over their private documents while also simplifying the process for banks to obtain the records needed for compliance

Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.

Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT

The industrial internet of things (IIoT) uses connected devices and sensors to improve efficiency in industry, but increased reliance on these systems makes them prone to faults. To ensure the reliability of IIoT systems, robust fault detection is necessary, which can be provided by artificial intelligence (AI). The current state-of-the-art approach for AI in IIoT is mainly focused on centralized learning, which is inefficient due to its high communication cost. Federated learning (FL) addresses this limitation by enabling distributed training without exposing individual information. In order to provide secure and efficient FL for the IIoT environment, several research studies have investigated the use of blockchain networks. However, most of these studies ignore the processing time metric, which is crucial for IIoT networks. In this paper, we propose a secure and efficient parameter aggregation technique that enhances trust, security, and privacy for IIoT. We use a lightweight smart contract deployed with a proof-of-authority (PoA)-based blockchain in combination with a Gaussian differential privacy mechanism. We evaluate our proposed system using a real bearing fault dataset and demonstrate that it is able to provide a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment with a private blockchain network.

Enhancing Industrial IoT Network Security through Blockchain Integration

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain

AbstractIn this paper, a hybrid blockchain‐based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway‐based Energy‐Aware Multi‐hop Routing (I‐HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.

Game-theoretic private blockchain design in edge computing networks

Considering the privacy challenges of secure storage and controlled flow, there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace. A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information. Traditional blockchains have similar faults, such as trustlessness, single-factor consensus, and heavily distributed ledger, preventing them from adapting to the heterogeneous and resource-constrained Internet of Things. In this paper, we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain. The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs. We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders. We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable. Then, we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival. To continuously validate a block, the metadata of the optimum rating, privacy, and proofs are extracted to store on a lightweight blockchain. Moreover, to increase resource utilization, surplus computing power is scheduled flexibly to enhance security by degrees. Finally, the evaluation results show the validity and efficiency of our model, thereby solving the collaboration dilemma in the private blockchain.